Domain Decomposition Methods

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15 papers1 paper in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

A Composable AI-Accelerated Iterative Solver for 3D-IC Thermal Modeling
Oct 1, 2026Yixing Li, Jiahang Zhou, Zhiyu Zeng +1Domain Decomposition Methods Electronic Design Automation
Cadence Design Systems · University of Notre Dame
Domain-decomposed Evolutional Deep Neural Network with Random Features for Transient Pressure Diffusion with Discontinuous and High-Contrast Coefficients
Sep 17, 2026Peiqi Li, Jie Chen, Hui Zhang +1Porous Media Flow Domain Decomposition Methods
Department of Applied Mathematics, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China · Department of Mathematical Sciences, University of Liverpool, Liverpool, L69 3BX, United Kingdom
Adaptive hybrid coupling with operator inference, the overlapping Schwarz alternating method and reinforcement learning
Sep 15, 2026Trishit Mondal, Irina Tezaur, Anthony GruberReinforcement Learning PDE Surrogate Modeling
Worcester Polytechnic Institute
Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
Sep 13, 2026Sidharth S. Menon, Irina Tezaur, Ameya D. JagtapPDE Solving Gradient Interference
Aerospace Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA. · Sandia National Laboratories, 7011 East Ave, Livermore, CA 94550, USA.
Scalable Multi-GPU Simulation of 3D Multicellular Growth with RNN-Based Workload Balancing
Aug 26, 2026Matvey Moisseyev, Huijing Du, Dandan Zheng +2Domain Decomposition Methods Recurrent Neural Networks
School of Computing, University of Nebraska–Lincoln · Department of Mathematics, University of Nebraska–Lincoln · Department of Radiation Oncology, University of Rochester Medical Center +2
Two-level domain-decomposition AdaGrad method for scalable training of graph neural networks
Aug 23, 2026Laurynas Varnas, Julien Herrmann, Alexander Heinlein +2Domain Decomposition Methods Graph Neural Networks
Institut de Recherche en Informatique de Toulouse, Toulouse, France. · Artificial and Natural Intelligence Toulouse Institute, Toulouse, France. · Centre National de la Recherche Scientifique, France. +3
Domain-Decomposed Randomized Neural Networks for Partial Differential Equations in Unbounded Domains
Jun 30, 2026Haixin Wang, Haoning Dang, Fei Wang +1Neural PDE Solvers PDE Solving
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. China. · School of Mathematics and Statistics & State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China.
Hierarchical Attention via Domain Decomposition
Jun 16, 2026Stephan Köhler, Oliver RheinbachLow-Rank Attention PDE Operator Learning
Faculty of Mathematics and Computer Science Technische Universität Bergakademie Freiberg, 09596 Freiberg, Germany
A Non-Monotone Preconditioned Trust-Region Method for Neural Network Training
May 14, 2026Andrea Angino, Bindi Çapriqi, Shega Likaj +2Neural Network Optimization Second-Order Optimization
UniDistance Suisse · King Abdullah University of Science and Technology · Universit`a della Svizzera italiana
Neural-Schwarz Tiling for Geometry-Universal PDE Solving at Scale
May 12, 2026Paolo Secchi, Daniel S. Balint, Marco MauriziNeural PDE Solvers PDE Solving
Imperial College London · Italian Institute of Artificial Intelligence
Observation-Aligned Two-Stage Domain Decomposition for Physics-Informed Traffic State Estimation with Sparse Fixed Sensors
May 8, 2026Eunhan Ka, Ludovic Leclercq, Satish V. UkkusuriTraffic Flow Estimation Domain Decomposition Methods
Lyles School of Civil and Construction Engineering, Purdue University, 610 Purdue Mall, West Lafayette, 47907, IN, United States · Univ Gustave Eiffel, ENTPE, LICIT-ECO7, Lyon, F-69675, France
Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies
Apr 7, 2026Prashant Kumar, Rajesh RanjanDomain Decomposition Methods Neural PDE Solvers
Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, India 208016
Hybrid coupling with operator inference and the overlapping Schwarz alternating method
Nov 20, 2025Irina Tezaur, Eric Parish, Anthony Gruber +3PDE Solving Reduced-Order Modeling
Sandia National Laboratories, Livermore, CA and Albuquerque, NM, USA · Rice University, Houston, TX, USA
Neural network-driven domain decomposition for efficient solutions to the Helmholtz equation
Nov 19, 2025Victorita Dolean, Daria Hrebenshchykova, Stéphane Lanteri +1Neural PDE Solvers PDE Solving
Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands · Atlantis project-team, Inria Research Center at Université Côte d’Azur, France · MACARON project-team, Université de Strasbourg, CNRS, Inria, IRMA, France
A Flow-rate-conserving CNN-based Domain Decomposition Method for Blood Flow Simulations
Sep 19, 2025Simon Klaes, Axel Klawonn, Natalie Kubicki +4PDE Solving PDE Surrogate Modeling
Department of Mathematics and Computer Science, University of Cologne, Germany · Center for Data and Simulation Science, University of Cologne, Germany · Institute for Advanced Simulation (IAS-2), Forschungszentrum Jülich, Germany +1

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A Composable AI-Accelerated Iterative Solver for 3D-IC Thermal Modeling

Yixing Li, Jiahang Zhou, Zhiyu Zeng, Xin Ai

Cadence Design Systems · University of Notre Dame

Domain Decomposition Methods Electronic Design Automation Neural PDE Solvers PDE Surrogate Modeling

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Accurate thermal analysis of heterogeneous 2.5D/3D-IC packages is essential yet computationally prohibitive. A single full-package FEM simulation can take hours, while AI-based surrogates treat the entire stack as a monolithic prediction target and must be retrained whenever the die count or topology changes. To address this limitation, this work proposes Domain-Decomposed AI-Accelerated Iterative Solver for Thermal Analysis (DAIST), a composable thermal solver that decomposes the global package simulation into block-level subdomain problems, replaces subdomain solvers with neural operators, and couples them through iterative exchanges of interfacial temperature and heat flux. This local-to-global architecture eliminates the topology lock-in of monolithic models: block-level neural operators can be directly reused in unseen package assemblies without retraining. The iterative coupling strategy further provides a controllable accuracy-runtime tradeoff, where the iteration budget can be adjusted to trade accuracy for runtime. Evaluated on a multi-chiplet system and an advanced packaging system, DAIST achieves up to

178\times

speedup over traditional FEM solvers with mean temperature errors of 0.068% and 0.323%, respectively, while demonstrating cross-topology reuse of block-level models across structurally distinct package assemblies.