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A Novel Method for Enforcing Exactly Dirichlet, Neumann and Robin Conditions on Curved Domain Boundaries for Physics Informed Machine Learning

https://doi.org/10.2139/ssrn.6463545
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FO-PINN: A First-Order formulation for Physics-Informed Neural Networks
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DGM: A deep learning algorithm for solving partial differential equations
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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Efficient deep learning techniques for multiphase flow simulation in heterogeneous porousc media
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PFNN: A penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries
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Adaptive deep density approximation for Fokker-Planck equations
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On Computing the Hyperparameter of Extreme Learning Machines: Algorithm and Application to Computational PDEs, and Comparison with Classical and High-Order Finite Elements
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Deep Neural Networks Based Temporal-Difference Methods for High-Dimensional Parabolic Partial Differential Equations
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A Method for Computing Inverse Parametric Pde Problems with Random-Weight Neural Networks
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A Unified Scalable Framework for Causal Sweeping Strategies for Physics-Informed Neural Networks (Pinns) and Their Temporal Decompositions
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Neural Galerkin schemes with active learning for high-dimensional evolution equations
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Multi-stage neural networks: Function approximator of machine precision
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Extreme learning machine: Theory and applications
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Physics Informed Extreme Learning Machine (PIELM)–A rapid method for the numerical solution of partial differential equations
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Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations
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Artificial neural networks for solving ordinary and partial differential equations
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VLSI implementation of locally connected neural network for solving partial differential equations
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D3M: A Deep Domain Decomposition Method for Partial Differential Equations
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Artificial Neural Network Method for Solution of Boundary Value Problems With Exact Satisfaction of Arbitrary Boundary Conditions
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Blending-Function Methods of Bivariate and Multivariate Interpolation and Approximation
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DeepXDE: A Deep Learning Library for Solving Differential Equations
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Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
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Deep Theory of Functional Connections: A New Method for Estimating the Solutions of Partial Differential Equations
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The Theory of Connections: Connecting Points
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The Multivariate Theory of Connections
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An overview on deep learning-based approximation methods for partial differential equations
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Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions
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Enforcing Exact Boundary and Initial Conditions in the Deep Mixed Residual Method
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Bridging Traditional and Machine Learning-Based Algorithms for Solving PDEs: The Random Feature Method
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