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Related papers: Learning electromagnetic fields based on finite el…

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In the design phase of an electrical machine, finite element (FE) simulation are commonly used to numerically optimize the performance. The output of the magneto-static FE simulation characterizes the electromagnetic behavior of the…

Machine Learning · Computer Science 2022-11-01 Vivek Parekh , Dominik Flore , Sebastian Schöps

In structural optimization, both parameters and shape are relevant for the model performance. Yet, conventional optimization techniques usually consider either parameters or the shape separately. This work addresses this problem by…

Optimization and Control · Mathematics 2025-03-18 Michael Wiesheu , Theodor Komann , Melina Merkel , Sebastian Schöps , Stefan Ulbrich , Idoia Cortes Garcia

Future e-mobility calls for efficient electrical machines. For different areas of operation, these machines have to satisfy certain desired properties that often depend on their design. Here we investigate the use of multipatch Isogeometric…

Numerical Analysis · Mathematics 2026-04-02 Peter Gangl , Ulrich Langer , Angelos Mantzaflaris , Rainer Schneckenleitner

Meta-materials are an important emerging class of engineered materials in which complex macroscopic behaviour--whether electromagnetic, thermal, or mechanical--arises from modular substructure. Simulation and optimization of these materials…

Machine Learning · Computer Science 2020-05-18 Alex Beatson , Jordan T. Ash , Geoffrey Roeder , Tianju Xue , Ryan P. Adams

This paper discusses and analyses two domain decomposition approaches for electromagnetic problems that allow the combination of domains discretised by either N\'ed\'elec-type polynomial finite elements or spline-based isogeometric…

Computational Engineering, Finance, and Science · Computer Science 2019-01-04 Annalisa Buffa , Jacopo Corno , Carlo de Falco , Sebastian Schöps , Rafael Vázquez

We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporate the momentum balance and constitutive relations into PINN,…

Machine Learning · Computer Science 2020-05-12 Ehsan Haghighat , Maziar Raissi , Adrian Moure , Hector Gomez , Ruben Juanes

Surrogate models for computational simulations are input-output approximations that allow computationally intensive analyses, such as uncertainty propagation and inference, to be performed efficiently. When a simulation output does not…

Computational Engineering, Finance, and Science · Computer Science 2014-08-05 Alex A. Gorodetsky , Youssef M. Marzouk

Precise magnetic field modeling is fundamental to the closed-loop control of electromagnetic navigation systems (eMNS) and the analytical Multipole Expansion Model (MPEM) is the current standard. However, the MPEM relies on strict physical…

Systems and Control · Electrical Eng. & Systems 2026-02-09 Antonio Bernardes , Jasan Zughaibi , Michael Muehlebach , Bradley J. Nelson

We review the recent development of machine-learning (ML) force-field frameworks for Landau-Lifshitz-Gilbert (LLG) dynamics simulations of itinerant electron magnets, focusing on the general theory and implementations of symmetry-invariant…

Strongly Correlated Electrons · Physics 2025-01-13 Sheng Zhang , Yunhao Fan , Kotaro Shimizu , Gia-Wei Chern

While interpolatory bases such as the Lagrange basis form the cornerstone of classical finite element methods, they have been replaced in the more general finite element setting of isogeometric analysis in favor of other desirable…

Numerical Analysis · Mathematics 2025-12-09 Yannis Voet , Espen Sande

Multiphysics simulation is critical for system-technology co-optimization (STCO) in chiplet-based design, but repeated finite-element solutions of PDE-governed problems are computationally expensive in parametric design exploration. This…

Machine Learning · Computer Science 2026-05-05 Xinyu Li , Jianhua Zhang , Liang Chen

Reconstruction of geometry based on different input modes, such as images or point clouds, has been instrumental in the development of computer aided design and computer graphics. Optimal implementations of these applications have…

Computer Vision and Pattern Recognition · Computer Science 2019-01-15 Jun Gao , Chengcheng Tang , Vignesh Ganapathi-Subramanian , Jiahui Huang , Hao Su , Leonidas J. Guibas

Surrogate models for partial-differential equations are widely used in the design of meta-materials to rapidly evaluate the behavior of composable components. However, the training cost of accurate surrogates by machine learning can rapidly…

Machine Learning · Computer Science 2020-11-04 Raphaël Pestourie , Youssef Mroueh , Thanh V. Nguyen , Payel Das , Steven G. Johnson

In the design optimization of metal forming, it is increasingly significant to use surrogate models to analyse the finite element analysis (FEA) simulations. However, traditional surrogate models using scalar based machine learning methods…

Computer Vision and Pattern Recognition · Computer Science 2020-10-08 Haosu Zhou , Qingfeng Xu , Nan Li

In recent years, Scientific Machine Learning (SciML) methods for solving partial differential equations (PDEs) have gained increasing popularity. Within such a paradigm, Physics-Informed Neural Networks (PINNs) are novel deep learning…

Numerical Analysis · Mathematics 2024-04-25 Pasquale Ambrosio , Salvatore Cuomo , Mariapia De Rosa

In implementations of the functional data methods, the effect of the initial choice of an orthonormal basis has not gained much attention in the past. Typically, several standard bases such as Fourier, wavelets, splines, etc. are considered…

Machine Learning · Statistics 2021-03-15 Rani Basna , Hiba Nassar , Krzysztof Podgórski

For many novel applications, such as patient-specific computer-aided surgery, conventional solution techniques of the underlying nonlinear problems are usually computationally too expensive and are lacking information about how certain can…

Machine Learning · Computer Science 2022-07-18 Saurabh Deshpande , Jakub Lengiewicz , Stéphane P. A. Bordas

Spatial and visual connectivity are important metrics when developing workplace layouts. Calculating those metrics in real-time can be difficult, depending on the size of the floor plan being analysed and the resolution of the analyses.…

Machine Learning · Computer Science 2020-01-01 Sherif Tarabishy , Stamatios Psarras , Marcin Kosicki , Martha Tsigkari

This contribution investigates the connection between Isogeometric Analysis (IgA) and the Partial Element Equivalent Circuit (PEEC) method for electrostatic problems. We demonstrate that using the spline-based geometry concepts from IgA…

Computational Engineering, Finance, and Science · Computer Science 2022-09-30 Riccardo Torchio , Maximilian Nolte , Sebastian Schöps , Albert E. Ruehli

Direct numerical simulation of hierarchical materials via homogenization-based concurrent multiscale models poses critical challenges for 3D large scale engineering applications, as the computation of highly nonlinear and path-dependent…

Computational Engineering, Finance, and Science · Computer Science 2022-12-29 Shiguang Deng