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09/13/2023
Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning
The recent surge in large-scale foundation models has spurred the develo...
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08/09/2023
Finite Element Operator Network for Solving Parametric PDEs
Partial differential equations (PDEs) underlie our understanding and pre...
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06/28/2023
Separable Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) have recently emerged as promis...
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11/16/2022
Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs
In this paper, we perform the convergence analysis of unsupervised Legen...
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11/16/2022
Separable PINN: Mitigating the Curse of Dimensionality in Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) have emerged as new data-driven...
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10/15/2022
Invertible Monotone Operators for Normalizing Flows
Normalizing flows model probability distributions by learning invertible...
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08/19/2022
Semi-analytic PINN methods for singularly perturbed boundary value problems
We propose a new semi-analytic physics informed neural network (PINN) to...
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07/26/2022
PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers
With the increases in computational power and advances in machine learni...
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07/21/2022
Unsupervised Legendre-Galerkin Neural Network for Stiff Partial Differential Equations
Machine learning methods have been lately used to solve differential equ...
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10/24/2020
Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation
Nonlinear differential equations are challenging to solve numerically an...
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10/20/2020
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods
Deep neural networks have achieved state-of-the-art performance in a var...
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07/12/2020
