Papers PDE Surrogate Modeling
“PDE Surrogate Modeling” 태그가 달린 논문 9편 · 필터 해제
Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems
Large-scale physical systems defined on irregular grids pose significant scalability challenges for deep learning methods, especially in the presence of long-range interactions and multi-scale coupling. Traditional appro…
Computational EfficiencyPDE Surrogate ModelingPhysical SimulationsDeep Operator Networks for Bayesian Parameter Estimation in PDEs
We present a novel framework combining Deep Operator Networks (DeepONets) with Physics-Informed Neural Networks (PINNs) to solve partial differential equations (PDEs) and estimate their unknown parameters. By integrating…
parameter estimationPDE Surrogate ModelingUncertainty QuantificationVariational InferenceExtreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow
A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions as described by the Cahn-Hilliard equation. A specialized, physics-inspired …
PDE Surrogate ModelingAero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to un…
Operator learningPDE Surrogate ModelingLearning to Predict Structural Vibrations
In mechanical structures like airplanes, cars and houses, noise is generated and transmitted through vibrations. To take measures to reduce this noise, vibrations need to be simulated with expensive numerical computation…
Operator learningPDE Surrogate ModelingUncertainty QuantificationScalable Transformer for PDE Surrogate Modeling
Transformer has shown state-of-the-art performance on various applications and has recently emerged as a promising tool for surrogate modeling of partial differential equations (PDEs). Despite the introduction of linear-…
PDE Surrogate ModelingLearning Neural PDE Solvers with Parameter-Guided Channel Attention
Scientific Machine Learning (SciML) is concerned with the development of learned emulators of physical systems governed by partial differential equations (PDE). In application domains such as weather forecasting, molecul…
PDE Surrogate ModelingWeather ForecastingConvolutional Neural Operators for robust and accurate learning of PDEs
Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been largely ignored in the context of learning so…
Operator learningPDE Surrogate ModelingTowards Multi-spatiotemporal-scale Generalized PDE Modeling
Partial differential equations (PDEs) are central to describing complex physical system simulations. Their expensive solution techniques have led to an increased interest in deep neural network based surrogates. However,…
PDE Surrogate Modeling