Abstract: Current studies of outdoor thermal comfort are limited to calculating thermal indices or interviewing people. The first method does not take into account the way people use this space, whereas the second one is limited to one particular study area. Simulating people’s thermal perception along with their activities in public urban spaces will help architects and city planners to test their concepts and to design smarter and more liveable cities. In this paper, we propose an agent-based modelling approach to simulate people’s adaptive behaviour in space. Two levels of pedestrian behaviour are considered: reactive and proactive, and three types of thermal adaptive behaviour of pedestrians are modelled with single-agent scenarios: speed adaptation, thermal attraction/repulsion and vision-motivated route alternation. An "accumulated heat stress" parameter of the agent is calculated during the simulation, and pedestrian behaviour is analysed in terms of its ability to reduce the accumulated heat stress. This work is the first step towards the "human component" in urban microclimate simulation systems. We use these simulations to drive the design of real-life experiments, which will help calibrating model parameters, such as the heat-speed response, thermal sensitivity and admissible turning angles.

Abstract: Animal groups provide paradigmatic examples of collective phenomena in which repeated interactions among individuals produce dynamic patterns and responses on a scale larger than individuals themselves. For instance, many swarming behaviors yield protective strategies for a groups undergoing a predator’s attack. The effectiveness of these evasive maneuvers is striking given: (i) the decentralized nature of such responses, (ii) the short time scales involved, and (iii) the competitive biological and physiological advantages of predators—e.g., in terms of size, speed, sensory capabilities—as compared to fleeing agents. Here, we report on results of agent-based simulations of collective anti-predatory response. Our prime goal is to gain insight into the nontrivial effect of sociality—a measure of the amount of social interaction—on the effectiveness of the collective response. Specifically, we characterize the responsiveness of the swarm by simulating a predator attack and measuring the survival rate of agents depending on their level of sociality for different interaction rules, based on either a metric or a topological interaction distance. Furthermore, evolutionary pressure selects strategies optimal for the individual and not necessarily for the group. This possibility has been explored by running evolutionary simulations. Interestingly, the results obtained clearly show the existence of an optimal anti-predatory response for a given amount of sociality, regardless of the interaction distance considered. The results of the evolutionary dynamics highlights the fact that the evolution of the distribution of sociality caused by the selective pressure of a predator’s attack has a phenomenology that cannot be derived from the short-time predator avoidance results.

Abstract: Overcrowding in mass rapid transit stations is a chronic issue affecting daily commute in Metro Manila, Philippines. As a high-capacity public transportation, the Metro Rail Transit has been operating at a level above its intended capacity of 350,000 passengers daily. Despite numerous efforts in implementing an effective crowd control scheme, it still falls short in containing the formation of crowds and long lines, thus affecting the amount of time before they can proceed to the platforms. A crowd dynamics model of commuters in one of the high-volume terminal stations, the Taft Ave station, was developed to help discover emergent behavior in crowd formation and assess infrastructure preparedness. The agent-based model uses static floor fields derived from the MRT3 live feed, and implements a number of social force models to optimize the path-finding of the commuter agents. Internal face validation, historical validation and parameter variability-sensitivity analysis were employed to validate the crowd dynamics model and assess different operational scenarios. It was determined that during peak hours, when the expected crowd inflow may reach up to 7,500 commuters, at least 11 ticket booths and 6 turnstiles should be open to have low turnaround times of commuters. For non-peak hours, at least 10 ticket booths and 5 turnstiles are needed to handle a crowd inflow reaching up to 5,000 commuters. In the current set-up, the usual number of ticket booths open in the MRT Taft Station is 11, and there are usually 6 turnstiles open. It was observed that as the crowd inside the station increases to 200-250 commuters, there is a significant increase in the increase rate of the turnaround times of the commuters, which signifies the point at which the service provided starts to degrade and when officials should start to intervene.

Abstract: The success of refugee support operations depends on the ability of humanitarian organizations and governments to deploy aid eectively. These operations require that decisions on resource allocation are made as quickly as possible in order to respond to urgent crises and, by antici- pating future developments, remain adequate as the situation evolves. Agent-based modeling and simulation has been used to understand the progression of past refugee crises, as well as a way to predict how new ones will unfold. In this work, we tackle the problem of refugee aid deployment as a variant of the Robust Facility Location Problem (RFLP). We present a serious video game that functions as an interface for an agent-based simulation run with data from past refugee crises. Having obtained good approximate solutions to the RFLP by implementing a game that frames the problem as a puzzle, we adapted its mechanics and interface to correspond to refugee situations. The game is intended to be played by both subject matter experts and the general public, as a way to crowd-source eective courses of action in these situations.

Abstract: This paper presents the research on the influence of obstacles on crowd dynamics. We have performed experiments for four base scenarios of interaction in crowd: unidirectional flow, bidirectional flow, merging flows and intersection. Movement of pedestrians has been studied in simple shape areas: straight corridor, T-junction and intersection. The volumes and basic directions of pedestrian flows were determined for each of the areas. Layout of physical obstacles has been built from different combinations of columns and barriers. In order to acquire characteristics of the crowd dynamics a set of simulations was conducted using PULSE simulation environment. In the result, we have managed to obtain several dependences between layout of obstacles and crowd dynamics were obtained.

Abstract: Simulation of age progression of 3D head models is an open problem in the field of computer graphics. Existing methods usually require a large set of training data, which may not be available. In this paper, a method for aging simulation of models created by deformation-based modeling is proposed that requires no training data. A user defines the position of wrinkles by selecting the position of endpoints of the desired wrinkles and the wrinkles are then generated using an erosion-inspired approach. The method can be used to simulate aging of any head model, however, if used for models created by deformations of a base model, the erosion factors can be calculated only for the base model and applied to the derived models. The results show that the approach is capable of creating visually plausible aged models.

Abstract: In this paper, we demonstrate how to extend perfect spatial hashing (PSH) to the problem domain of indexing nodes in a graph that represents of Super Carbon Nanotubes (SCNTs). The goal of PSH is to hash multidimensional data without collisions. Since PSH results from the research on computer graphics, its principles and methods have only been tested on 2− and 3−dimensional problems. In our case, we need to hash up to 28 dimensions. In contrast to the original applications of PSH, we do not focus on GPUs as target hardware but on an efficient CPU implementation. Thus, this paper highlights the extensions to the original algorithm to make it suitable for higher dimensions and the representation of SCNTs. Comparing the compression and performance results of the new PSH based graphs and a structure-tailored custom data structure in our parallelized SCNT simulation software, we find, that PSH in some cases achieves better compression by a factor of 1.7 while only increasing the total runtime by several percent. In particular, after our extension, PSH can also be employed to index sparse multidimensional scientific data from other domains.

Abstract: Nowadays, as large amounts of data are generated, either from experiments, satellite imagery or via simulations, access to this data becomes challenging for users who need to further process them, since existing data management makes it difficult to effectively access and share large data sets. In this paper we present an approach to enabling easy and secure collaborations based on the state of the art authentication and authorization mechanisms, advanced group/role mechanism for flexible authorization management and support for identity mapping between local systems, as applied in an eventually consistent distributed file system called Onedata.

Abstract: Predicting the compressive strength of concrete is an essential task in the construction process, since a prior knowledge on such information helps enhancing speed and quality of the process. Recently, many computational methods and techniques have been developed to predict distinct properties of concrete. However, a practical use of these solutions requires a high degree of engineering expertise and programming skills. Alternatively, this work advocates that software packages with off-the-shelf data mining algorithms can empower researchers and engineers on this task, while demanding less effort. In this direction, we present a detailed study on the use of Weka, evaluating different regression algorithms for predicting the compressive strength of concrete. Using the most complete dataset available at the UCI dataset repository, we demonstrate that most of the techniques available in Weka produces results close to the best ones reported in the literature. For instance, most of the evaluated predicting models generates a Mean Absolute Error (MAE) inferior to 10, while the best result found is 8. Moreover, by fine-tuning the parameters of the regression algorithm Bagging with REPTree, we achieved a MAE value inferior to 3.3 for the evaluated dataset. Hence, the process considered in this study is also useful as a guideline to devise new computational models based on off-the-shelf data mining algorithms.

Abstract: Large scale time-dependent particle simulations can generate massive amounts of data, making it so that storing the results is often the slowest phase and the primary time bottleneck of the simulation. Furthermore, analysing this amount of data with traditional tools has become increasingly challenging, and it is often virtually impossible to have a visual representation of the full set. We propose a novel architecture that integrates a HPC-based multi-physics simulation code, a NoSQL database, and a data analysis and visualisation application. The goals are two: On the one hand, we aim to speed up the simulations taking advantage of the scalability of key-value data stores, while at the same time enabling real-time approximated data visualisation and interactive exploration. On the other hand, we want to make it efficient to explore and analyse the large data base of results produced. Therefore, this work represents a clear example of integrating High Performance Computing with High Performance Data Analytics. Our prototype proves the validity of our approach and shows great performance improvements. Indeed, we reduced by 67.5% the time to store the simulation while we made real-time queries run 52 times faster than alternative solutions.

Abstract: [No abstract available]

Abstract: Obtaining meaningful computational science project assignments for students is always a challenge in a variety of courses from modeling and simulation to high performance computing to many other mathematics and computer science subjects. Often, the instructor desires projects that involve interesting applications, are challenging enough for teams of students while not being too difficult, have a suitable time commitment, are new, are instructive, and cover appropriate concepts. The Shiflets coauthored Introduction to Computational Science: Modeling and Simulation for the Sciences, 2nd edition (Princeton U. Press, 2014), the only textbook of its kind designed specifically for an introductory course in the computational science and engineering curriculum. The textbook, which a variety of courses on the undergraduate and graduate level use, includes numerous such projects. Additional projects are now publically available on the textbook’s website (https://ics.wofford-ecs.org/additional-projects) for anyone to use; and instructors can obtain solutions to a selection of the assignments from the authors. The problems involve a number of important computational science concepts and approaches, such as system dynamics modeling, empirical modeling, random walks, cellular automaton simulations, sensitivity analysis, agent-based simulations, age- and stage-structured modeling, Markov chains, and matrix modeling of social networks. Applications include invasive species, rumor spreading, juvenile delinquency, fur patterns, condor populations, rice viruses, toxin-producing micro-organisms, coral bleaching, brown bears, neuron signals, and succession. The talk will discuss some of these applications and approaches and present representative models and simulations to solve the problems.

Abstract: The Institute for Advanced Computational Science (IACS) is Stony Brook University’s flagship organization for teaching and promoting computational and data science inside and outside of campus. It is an interdisciplinary organization that brings together faculty and students from applied mathematics, statistics, physics, chemistry, marine and atmospheric sciences, engineering, ecology and evolution, sociology, linguistics, music, medicine, and many others. We currently offer a Certificate in Data & Computing for Scientists and Engineers and a NSF-sponsored training program (STRIDE: Science Training and Research to Inform DEcisions) for doctoral students. Masters-level and undergraduate programs are being developed. This poster overviews and summarizes the pedagogical strategies for computational science education at IACS and Stony Brook University. We focus on training domain experts who are able and skilled at using high-performance computational resources to generate, analyze, and interpret data to solve research problems. We stress interdisciplinary communication and work closely with the Alan Alda Center for Communicating Science to help our researchers bridge these gulfs. Other IACS initiatives, such as computational science “bootcamps” for medical researchers, primary school teachers, and high-school students will also be discussed.

Abstract: We are midway through the development of writing four textbooks and four open-content computer lab manuals with supporting materials for a new upper-division undergraduate curriculum in Applied and Computational Mathematics that will modernize the mathematics major and better integrate it with the broader STEM community. The new curriculum is being piloted at Brigham Young University as a new degree program. The new curriculum brings mathematical analysis, algorithm design, optimization, data science and mathematical modeling into the forefront of interdisciplinary study in the pure and applied sciences. Scientific computing is taught in the context of big data and high performance computing. The textbooks are being published by the Society of Industrial and Applied Mathematics (SIAM), and both the lab manuals and supporting materials will be made freely available online. The project creates a modern undergraduate curriculum which provides the foundation that students need to become world-class problem solvers and interdisciplinary innovators. This should increase student interest and participation in applied and computational mathematics, and expand the pipeline of young scholars in the mathematical sciences who are well equipped to face the challenges of the 21st Century and become leaders in the globally competitive STEM workforce.

Abstract: Over the past decade, a variety of free computational modeling tools have become available for use in secondary school and introductory college courses. These tools provide an excellent introduction to computation for students who have yet to develop skill at or interest in programming. Once exposed to these tools through modeling projects, students have reported that they understand the value of computation in solving problems, and also the limitations of the tools -- which highlights the need to delve further into computational techniques. In this session, an overview of several such tools will be provided, along with examples of modeling and simulation projects that have been used mathematics courses at the secondary and college level. The tools that have been used successfully with students at these levels include InsightMaker (web-based systems and agent modeling); VensimPLE (systems modeling); NetLogo (agent modeling); Gephi (network and graph analysis); WolfraAlpha (web-based symbolic computation); DataFlyer and Desmos (web-based graphing and data fitting).

Abstract: We will present our experiences in teaching science courses with computational perspective. In 2011, when our efforts have started, the only available solution which would provide web based mathematical experimentation environment for students was SageMath notebook (sagenb). We have decided to provide at our faculty for all students a central installation of sagenb and to prepare teaching materials for most of courses. Now, the system is being displaced by a modern Jupyter notebook which provided SageMath kernel but also pure scientific Python ecosystem. In this talk we present benefits and technical challenges of creating such infrastructure both for universities as well as for schools.

Abstract: The paper introduces a new taxonomy of ill-posed parametric inverse problems, formulated as global optimization ones. It systematizes irremediable problems, which appear quite often in real life but cannot be solved using the regularization method. The paper shows also a new way of solving irremediable inverse problems by a complex memetic approach including genetic computation with adoptive accuracy, random sample clustering and a sophisticated local approximation of misfit plateau region. Finally, we use a benchmark function featuring cross-shaped plateau to discuss some factors that influence the quality of plateau shape approximation.

Abstract: Memetic agent-based paradigm, which combines evolutionary computation and local search techniques in one of promising meta-heuristics for solving large and hard discrete problem such as Low Autocorrellation Binary Sequence (LABS) or optimal Golomb-ruler (OGR). In the paper as a follow-up of the previous research, a short concept of hybrid agent-based evolutionary systems platform, which spreads computations among CPU and GPU is shortly introduced. The main part of the paper presents an efficient parallel GPU implementation of LABS local optimization strategy. As a means for comparison, speed-up between GPU implementation and CPU sequential and parallel versions are shown. This constitutes a promising step toward building hybrid platform that combines evolutionary meta-heuristics with highly efficient local optimization of chosen discrete problems.

Abstract: We present a discrete entropy inequality that exhibits versatile uses in convection dominated problems. Much like the thermodynamic entropy inequality, the sign of this so-called discrete entropy production allows us to determine unphysical regions in the numerical solution without any a-priori knowledge of the solution. Further, the sign of the discrete production also functions as an excellent indicator for mesh adaptation in convection-diffusion and other singular perturbation problems. We also show preliminary results for how the operator can be used to derive robust schemes for convection dominated problems. All the above applications i.e. (a) Detecting unphysical numerical behavior, (b) mesh adaptation and (c) stabilization, are robust in that they are achieved without any ad-hoc, user introduced, parameters making the applications robust. We show a range of numerical results that exhibit the effcacy of the operator.

Abstract: This paper presents an idea of a multi-agent decision support system. Agent-based technology allows for decentralized problem solving and creating complex decision support systems, mixing various processing techniques, such as simulation, reasoning and machine learning and allows for distributed knowledge. Our main contribution is an agent-based architecture for decision support systems which is an agent-based implementation of a labeled deductive system. Such approach allows to decompose an inference algorithm into separate modules and distribute knowledge base into parts. The system is tested on a domain of material choice support for casting.

Abstract: CFD (Computational Fluids Dynamic) is an important branch of fluid dynamics. It applies various kinds of discrete mathematical method to analyze and simulate problems in fluid mechanics with the use of computer. During the computation, huge computational tasks on a single CPU often makes it very inefficient to get the result, so there is an increasing number of application of parallel computation in CFD. With more powerful computing capability and lower price, GPU (Graphic Processing Unit) has become a better solution for parallel computing than CPU in recent years. In this paper, we implemented the HSMAC and SIMPLE algorithms on GPU. For the simulation of 2D lid-driven cavity flow, the GPU version could get a speedup up to 58x and 21x respectively with double precision, and 78x and 32x with single precision, compared to the sequential CPU version. It demonstrates a good prospects of GPU acceleration of CFD algorithms.

Abstract: Numerical modeling of gas and liquid flows and, in particular, multiphase mediums, is a promising direction of scientific investigations and development of industrial apparatus. Experimental approach in the field of multiphase flows is not always capable of obtaining required information about the flow structure due to the excessive amount of physical phenomena involved. Numerical simulations of real flows with inclusion of all processes and phenomena or on real-scale geometries are very resource-demanding and are not feasible on stand-alone personal working stations. Thus, applying parallelization techniques at the existing solution algorithms with the means of OpenMP library alongside with supercomputer technologies can reduce computational time and can help with simulations of complex flows on the systems with shared memory. The study presents the description of the previously developed mathematical model of polydisperse multiphase flows, numerical algorithm for the solution of governed equations of the model and description of the numerical method. Simulations by the means of the proposed algorithm were carried out for the case of polydisperse bubbly flow inside water-filled rectangular column. Results presented in the paper, which are obtained during numerical experiments carried out on the “SC Politechnichesky”, comprise of the obtained flow field and bubble distributions and of the dependencies of program working time on the amount of threads and model parameters.

Abstract: In this work, a hybrid particle-grid method coupled with a penalization technique is introduced in order to compute Direct Numerical Simulations in three dimensions. The method is validated with the litterature for the flow past a sphere and a hemisphere. The approach is extented to solid-porous-fluid media and applied to passive flow control for the hemisphere using porous coatings.

Abstract: This paper presents a numerical study of the gravity-driven motion of single bubbles and bubble swarms through a vertical channel, using High Performance Computing (HPC) and Direct Numerical Simulation (DNS) of the Navier-Stokes equations. A systematic study of the wall effect on the motion of single deformable bubbles is carried out for confinement ratios CR={2,4,6}. Then, the rising motion of a swarm of deformable bubbles in a vertical channel is researched, for void fractions alpha={8.33%,12.5%}. These simulations are carried out in the framework of a novel multiple marker interface capturing approach, where each bubble is represented by a conservative level-set function. This method has the ability to avoid the numerical and potentially unphysical coalescence of the bubbles, allowing for the collision of the fluid particles as well as long time simulations of bubbly flows. Present simulations are performed in a periodic vertical domain discretized by 2e6 control volumes (CVs) up to 21e6 CVs, distributed in 128 up to 2048 processors. Collective and individual behaviour of the bubbles are characterized and compared against previous results from the literature.

Abstract: This work deals with an extension of the Path Tubes method for the solution of the timedependent Navier-Stokes equations for an incompressible Newtonian fluid. The resulting technique Departing from a physically intuitive methodology based on the theoretical basis of the mechanics of continuous media, a robust numerical technique is obtained. This version of the Path Tubes method draws on a semi-Lagrangian time-discretization employs the Reynolds’ transport theorem, and a localization approach, to establish an implicit semi-Lagrangian algorithm that allows the use of classical schemes for spatial discretization, such as central-difference formulas, without the need to use upwind techniques, or high-order corrections for time derivatives. Some of the extensive numerical tests are shown herein, in particular for Reynolds’ numbers typical of advection dominated flows. The tests are shown to be accurate and perform well even for coarse grids.

Abstract: A new second-order numerical scheme based on an operator splitting is proposed for the Godunov-Peshkov-Romenski model of continuum mechanics. The homogeneous part of the system is solved with a finite volume method based on a WENO reconstruction, and the temporal ODEs are solved using some analytic results presented here. Whilst it is not possible to attain arbitrary-order accuracy with this scheme (as with ADER-WENO schemes used previously), the attainable order of accuracy is often sufficient, and solutions are computationally cheap when compared with other available schemes. The new scheme is compared with a second-order ADER-WENO scheme for various test cases, and a convergence study is undertaken to demonstrate its order of accuracy.

Abstract: Within diffuse interface models for multiphase problems, the curvature of the phase boundary can be expressed as the difference of two terms, a Laplacian and a second, gradient, term of the diffuse interface variable, φ. In phase field simulations of microstructure evolution, the second term is often replaced by f'(φ) =∂f/∂φ, where f(φ) is the potential function in the free energy functional of the underlying physical model. We show here that this replacement systematically deteriorates the accuracy in local curvature evaluation as compared to a discretized evaluation of the second term. Analytic estimates reveal that the discretization errors in the Laplacian and in the second term have roughly the same spatial dependence across the interface, thus leading to a cancellation of errors in κ. This is confirmed in a test case, where the discretization error can be determined via comparison to the exact solution. If, however, the second term is replaced by a quasi exact expression, the error in ∆φ enters κ without being compensated and can obscure the behavior of the local curvature. Due to the antisymmetric variations of this error term, approaches using the average curvature, as obtained from an integral along the interface normal, are immune to this problem.

Abstract: Astrophysical phenomena cover many order of magnitude in spatial and temporal scales. An additional complexity is introduced by the multi-physics aspects of the Universe. We present the Astrophysical Multipurpose Software Environment (AMUSE), which was designed specifically to allow researchers to simulate these processes on high-performance architectures. In AMUSE subgrid physical phenomena can be taken into account explicitly. The coupling across scales and across physical domains is realized by means of operator splitting. In multi-scale simulations, when the underlying physics shares the same Hamiltonian, we demonstrate that this coupling strategy captures the right physics to second order. When employing the operator splitting strategy across discipline we validate the results by comparison with historic results. Simulation projects can be setup in AMUSE in a declarative fashion in which the coupling strategies are described at a meta level. These descriptions allow for the strict separation of individual modules for multi-scale and multi-domain simulations in the form of patterns. In this study we describe how these patterns are implemented in AMUSE and where they can be used to help the modeling celestial phenomena.

Abstract: Improving operating room (OR) management in large hospitals has been a challenging problem that remains largely unresolved. Fifty percent of hospital income depends on OR activities and among the main concerns in most institutions is to improve the efficiency of a large OR suite that. We advocate that optimizing surgical flow in large OR suites is a complex multifactorial problem with an underlying multiscale structure. Numerous components of the system can combine nonlinearly result in the large accumulated delays observed in daily clinical practice. We propose a multiscale agent-based model (ABM) of surgical flow. We developed a smartOR system that utilizes a dedicated network of non-invasive, wireless sensors to automatically track the state of the OR and accurately computes major indicators of performances such as turnover time between procedures. We show that our model can fit these time measurements and that a multiscale description of the system is possible. We will discuss how this model can be used to quantify and target the main limiting factors in optimizing OR suite efficiency.

Abstract: A powerful way to deal with a complex system is to build a coarse-grained model capable of catching its main physical features, while still being computationally affordable. Inevitably, such coarse-grained models introduce a set of phenomenological parameters, which are often not easily deducible from the underlying atomistic system. We present a novel approach to the calculation of these parameters, based on the recently introduced variationally enhanced sampling method. It allows us to obtain the parameters from atomistic simulations, providing thus a direct connection between the microscopic and the mesoscopic scale. The coarse-grained model we consider is that of Ginzburg-Landau, valid around a second order critical point. In particular we use it to describe a Lennard-Jones fluid in the region close to the liquid-vapor critical point. The procedure is general and can be adapted to other coarse-grained models.

Abstract: A deterministic technique for fast surrogate-assisted multi-objective design optimization of antennas in highly-dimensional parameters spaces has been discussed. In this two-stage approach, the initial approximation of the Pareto set representing the best compromise between conflicting objectives is obtained using a bisection algorithm which finds new Pareto-optimal designs by dividing the line segments interconnecting previously found optimal points, and executing poll-type search that involves Pareto ranking. The initial Pareto front is generated at the level of the coarsely-discretized EM model of the antenna. In the second stage of the algorithm, the high-fidelity Pareto designs are obtained through optimization of corrected local-approximation models. The considered optimization method is verified using a 17-variable uniplanar antenna operating in ultra-wideband frequency range. The method is compared to three state-of-the-art surrogate-assisted multi-objective optimization algorithms.

Abstract: In the present paper, an efficient parallel method for solving complex multicriterial optimization problems, which the optimality criteria can be multiextremal, and the computing of the criteria values can require a large amount of computations in, is proposed. The proposed approach is based on the reduction of the multicriterial problems to the global optimization ones using the minimax convolution of the partial criteria, the dimensionality reduction with the use of the Peano space-filling curves, and the application of the efficient parallel information-statistical global optimization methods. The intensive use of the search information obtained in the course of computations is provided when conducting the computations. The results of the computational experiments demonstrated such an approach to allow reducing the computation costs of solving the multicriterial optimization problems essentially - tens and hundreds times.

Abstract: In this paper, we propose the use of a surrogate model based on mixtures of liner Taylor polynomials for Trust Region methods. The main objective of this model is to reduce the myopia presented in surrogate models based on single low-order Taylor expansions by which, the number of iterations during the optimization process of Trust Region based methods can be increased. The proposed model is built as follows: points are sampled from the search space, at each sampled point a surrogate model of the cost function is built by using a linear Taylor polynomial and then, cost functions can be locally approximated via a convex combination of such surrogate models. The Trust Region framework is then utilized in order to validate the quality of the proposed model. Even more, this model is proven to be fully linear which guaranties the global convergence of Trust Region methods to local optimum solutions. Experimental tests are performed making use of the three-dimensional variational optimization problem from data assimilation with an atmospheric general circulation model. The results reveal that, the use of our proposed surrogate model can improve the quality of the local approximations and even more, their use can decrease the number of iterations needed in order to obtain accurate solutions.

Abstract: In this paper, we discuss a computationally efficient approach to expedite design optimization of broadband hybrid couplers occupying a minimized substrate area. Structure size reduction is achieved here by decomposing an original coupler circuit into low- and high-impedance components and replacing them with electrically equivalent slow-wave lines with reduced physical dimensions. The main challenge is reliable design of computationally demanding low-impedance slow-wave structures that feature a quasi-periodic circuit topology for wideband operation. Our goal is to determine an adequate number of recurrent unit elements as well as to adjust their designable parameters so that the coupler footprint area is minimal. The proposed method involves using surrogate-based optimization with a reconfigurable co-simulation coarse model as the key component enabling design process acceleration. The latter model is composed in Keysight ADS circuit simulator from multiple EM-evaluated data blocks of the slow-wave unit element and theory-based feeding line models. The embedded optimization algorithm is a trust-region-based gradient search with coarse model Jacobian estimation. We exploit a penalty function approach to ensure that the electrical conditions for the slow-wave lines are accordingly satisfied, apart from explicitly minimizing the area of the coupler. The effectiveness of the proposed technique is demonstrated through a design example of two-section 3-dB branch-line coupler. For the given example, we obtain nine circuit design solutions that correspond to the compact couplers whose multi-element slow-wave lines are composed of unit cells ranging from two to ten.

Abstract: Fast and accurate airfoil design under uncertainty using nonintrusive polynomial chaos expansions and utility functions is proposed. The NIPC expansions provide a means to efficiently and accurately compute statistical information for a given set of input variables with associated probability distribution. Utility functions provide a way to rigorously formulate the design problem. In this work, these two methods are integrated for the design of airfoil shapes under uncertainty. The proposed approach is illustrated on a numerical example of lift-constrained airfoil drag minimization in transonic viscous flow using the Mach number as an uncertain variable. The results show that compared with the standard problem formulation the proposed approach yields more robust designs. In other words, the designs obtained by the proposed approach are less sensitive to variations in the uncertain variables than those obtained with the standard problem formulation.

Abstract: Interest in simulation based screening of soft materials for desired chemical and physical properties has grown in recent years, which promises a reduced time to discovery and significantly lower costs than experimentation. While programmatically generating simulator inputs is fairly straightforward for crystals or small molecules, this is still challenging for complex (connected) molecular systems in general, and soft materials in particular. To tackle this challenge, this work presents a domain specific modeling environment that (1) features a domain specific modeling language (DSML), capable of describing classes of molecular systems in a hierarchical, component based model, with a rich set of composition operators, and support for parameterizable systems though generative modeling; (2) a browser-based, intuitive graphical user interface; (3) support for online collaboration, sharing of components, support for version control with history and branching; and (4) an interpreter to visualize components and molecular systems, and to generate output understood by major molecular dynamics (MD) simulator tools. The paper includes a case study demonstrating the use of the modeling environment to build a generative model of a parameterizable nanostructure, to feed input to a simulation based screening workflow of nanolubrication materials.

Abstract: Microscopic structure of a fully hydrated di-palmytoil-phosphatidyl-choline lipid bilayer mem- brane in the liquid-crystalline phase has been analyzed with all-atom molecular dynamics sim- ulations based on the recently parameterized CHARMM36 force field. Within the membrane a single molecule of the alpha-aminoacid tryptophan (precursor of important neurotransmitters such as serotonin and melatonin) has been embedded and his structure and binding sites have been explored. In addition, properties such as radial distribution functions, energy and pressure profiles and the potentials of mean force of water-tryptophan and lipid-tryptophan have been evaluated. It has been observed that tryptophan tends to be close to the lipid headgroups but that it can be fully hydrated during short time intervals of the order of 1 ns. This would indicate that hydrophobic forces as well as the attraction of tryptophan to polar sites of the lipids play a significant role in the definition of the structure and binding states of tryptophan.