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Found 14 papers, 1 papers with code
Geometry encoding for numerical simulations
We present a notion of geometry encoding suitable for machine learning-based numerical simulation.
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization
The two solver characteristics that have been adopted in this work are: 1) the use of discretization-based schemes to approximate spatio-temporal partial derivatives and 2) the use of iterative algorithms to solve linearized PDEs in their discrete form.
An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient
Specifically, a hybrid framework of Auto Encoder (AE) and Image Gradient (IG) based network is designed.
Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization
We achieve this by training multiple networks, each learning a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm.
ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs
In machine learning applications, 3-D surfaces are most suitably represented with point clouds or meshes and learning representations of interacting geometries form point-based representations is challenging.
A Latent space solver for PDE generalization
In this work we propose a hybrid solver to solve partial differential equation (PDE)s in the latent space.
One-shot learning for solution operators of partial differential equations
Discovering governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in a variety of areas of science and engineering.
A composable autoencoder-based iterative algorithm for accelerating numerical simulations
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.
A composable autoencoder-based algorithm for accelerating numerical simulations
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.
A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods
Based on recent advances in scientific deep learning for operator regression, we propose HINTS, a hybrid, iterative, numerical, and transferable solver for differential equations.
A Thermal Machine Learning Solver For Chip Simulation
Thermal analysis provides deeper insights into electronic chips behavior under different temperature scenarios and enables faster design exploration.
A composable machine-learning approach for steady-state simulations on high-resolution grids
The numerical experiments show that our approach outperforms ML baselines in terms of 1) accuracy across quantitative metrics and 2) generalization to out-of-distribution conditions as well as domain sizes.
NLP Inspired Training Mechanics For Modeling Transient Dynamics
In recent years, Machine learning (ML) techniques developed for Natural Language Processing (NLP) have permeated into developing better computer vision algorithms.
Diffusion model based data generation for partial differential equations
In a preliminary attempt to address the problem of data scarcity in physics-based machine learning, we introduce a novel methodology for data generation in physics-based simulations.
