Kernel-Adaptive PI-ELMs for Forward and Inverse Problems in PDEs with Sharp Gradients
Fuente:
arXiv
Saved in:
| Main Authors: | Dwivedi, Vikas, Srinivasan, Balaji, Sigovan, Monica, Sixou, Bruno |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEs
by: Dwivedi, Vikas, et al.
Published: (2025)
by: Dwivedi, Vikas, et al.
Published: (2025)
Meta-Learned Basis Adaptation for Parametric Linear PDEs
by: Dwivedi, Vikas, et al.
Published: (2026)
by: Dwivedi, Vikas, et al.
Published: (2026)
Soft Partition-based KAPI-ELM for Multi-Scale PDEs
by: Dwivedi, Vikas, et al.
Published: (2026)
by: Dwivedi, Vikas, et al.
Published: (2026)
Curriculum Learning-Driven PIELMs for Fluid Flow Simulations
by: Dwivedi, Vikas, et al.
Published: (2025)
by: Dwivedi, Vikas, et al.
Published: (2025)
Distributed physics informed neural network for data-efficient solution to partial differential equations
by: Dwivedi, Vikas, et al.
Published: (2019)
by: Dwivedi, Vikas, et al.
Published: (2019)
PICS in Pics: Physics Informed Contour Selection for Rapid Image Segmentation
by: Dwivedi, Vikas, et al.
Published: (2023)
by: Dwivedi, Vikas, et al.
Published: (2023)
Deep vs. Shallow: Benchmarking Physics-Informed Neural Architectures on the Biharmonic Equation
by: Srinivasan, Akshay Govind, et al.
Published: (2025)
by: Srinivasan, Akshay Govind, et al.
Published: (2025)
Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs
by: Srinivasan, Akshay Govind, et al.
Published: (2026)
by: Srinivasan, Akshay Govind, et al.
Published: (2026)
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
by: Wu, Tailin, et al.
Published: (2024)
by: Wu, Tailin, et al.
Published: (2024)
Towards Fast Option Pricing PDE Solvers Powered by PIELM
by: Srinivasan, Akshay Govind, et al.
Published: (2025)
by: Srinivasan, Akshay Govind, et al.
Published: (2025)
Global versus Local: Evaluating AlexNet Architectures for Tropical Cyclone Intensity Estimation
by: Dwivedi, Vikas
Published: (2024)
by: Dwivedi, Vikas
Published: (2024)
Gaussian Process Regression for Inverse Problems in Linear PDEs
by: Li, Xin, et al.
Published: (2025)
by: Li, Xin, et al.
Published: (2025)
Unifying and extending Diffusion Models through PDEs for solving Inverse Problems
by: Dasgupta, Agnimitra, et al.
Published: (2025)
by: Dasgupta, Agnimitra, et al.
Published: (2025)
Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems
by: Long, Da, et al.
Published: (2024)
by: Long, Da, et al.
Published: (2024)
Heat Kernel Goes Topological
by: Krahn, Maximilian, et al.
Published: (2025)
by: Krahn, Maximilian, et al.
Published: (2025)
Latent Neural Operator for Solving Forward and Inverse PDE Problems
by: Wang, Tian, et al.
Published: (2024)
by: Wang, Tian, et al.
Published: (2024)
Constrained Particle Seeking: Solving Diffusion Inverse Problems with Just Forward Passes
by: Dou, Hongkun, et al.
Published: (2026)
by: Dou, Hongkun, et al.
Published: (2026)
Generalized Kernel Thinning
by: Dwivedi, Raaz, et al.
Published: (2021)
by: Dwivedi, Raaz, et al.
Published: (2021)
Kernel Thinning
by: Dwivedi, Raaz, et al.
Published: (2021)
by: Dwivedi, Raaz, et al.
Published: (2021)
Adaptive Kernel Selection for Stein Variational Gradient Descent
by: Melcher, Moritz, et al.
Published: (2025)
by: Melcher, Moritz, et al.
Published: (2025)
BENO: Boundary-embedded Neural Operators for Elliptic PDEs
by: Wang, Haixin, et al.
Published: (2024)
by: Wang, Haixin, et al.
Published: (2024)
Supervised Kernel Thinning
by: Gong, Albert, et al.
Published: (2024)
by: Gong, Albert, et al.
Published: (2024)
Robust Non-negative Proximal Gradient Algorithm for Inverse Problems
by: Wang, Hanzhang, et al.
Published: (2025)
by: Wang, Hanzhang, et al.
Published: (2025)
A Deep Neural Network Framework for Solving Forward and Inverse Problems in Delay Differential Equations
by: Wang, Housen, et al.
Published: (2024)
by: Wang, Housen, et al.
Published: (2024)
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
by: Liu, Xu, et al.
Published: (2022)
by: Liu, Xu, et al.
Published: (2022)
Convolution Operator Network for Forward and Inverse Problems (FI-Conv): Application to Plasma Turbulence Simulations
by: Chen, Xingzhuo, et al.
Published: (2026)
by: Chen, Xingzhuo, et al.
Published: (2026)
TAEN: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problems
by: Nguyen, Hai V., et al.
Published: (2024)
by: Nguyen, Hai V., et al.
Published: (2024)
Toward Efficient Kernel-Based Solvers for Nonlinear PDEs
by: Xu, Zhitong, et al.
Published: (2024)
by: Xu, Zhitong, et al.
Published: (2024)
Moonwalk: Inverse-Forward Differentiation
by: Krylov, Dmitrii, et al.
Published: (2024)
by: Krylov, Dmitrii, et al.
Published: (2024)
Consistency Regularised Gradient Flows for Inverse Problems
by: Spagnoletti, Alessio, et al.
Published: (2026)
by: Spagnoletti, Alessio, et al.
Published: (2026)
CoCoGen: Physically-Consistent and Conditioned Score-based Generative Models for Forward and Inverse Problems
by: Jacobsen, Christian, et al.
Published: (2023)
by: Jacobsen, Christian, et al.
Published: (2023)
GenPANIS: A Latent-Variable Generative Framework for Forward and Inverse PDE Problems in Multiphase Media
by: Chatzopoulos, Matthaios, et al.
Published: (2026)
by: Chatzopoulos, Matthaios, et al.
Published: (2026)
Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
by: Sahin, T., et al.
Published: (2023)
by: Sahin, T., et al.
Published: (2023)
Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning
by: Krishnamurthy, Vikram, et al.
Published: (2026)
by: Krishnamurthy, Vikram, et al.
Published: (2026)
Inverse Problems with Learned Forward Operators
by: Arridge, Simon, et al.
Published: (2023)
by: Arridge, Simon, et al.
Published: (2023)
Exact Evaluation of the Accuracy of Diffusion Models for Inverse Problems with Gaussian Data Distributions
by: Pierret, Emile, et al.
Published: (2025)
by: Pierret, Emile, et al.
Published: (2025)
Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems trained with Gradient Descent
by: Buskulic, Nathan, et al.
Published: (2024)
by: Buskulic, Nathan, et al.
Published: (2024)
Lotka-Sharpe Neural Operators for Control of Population PDEs
by: Krstic, Miroslav, et al.
Published: (2026)
by: Krstic, Miroslav, et al.
Published: (2026)
Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator
by: Mukherjee, Amartya, et al.
Published: (2024)
by: Mukherjee, Amartya, et al.
Published: (2024)
GCSAM: Gradient Centralized Sharpness Aware Minimization
by: Hassan, Mohamed, et al.
Published: (2025)
by: Hassan, Mohamed, et al.
Published: (2025)
Similar Items
-
Gated X-TFC: Soft Domain Decomposition for Forward and Inverse Problems in Sharp-Gradient PDEs
by: Dwivedi, Vikas, et al.
Published: (2025) -
Meta-Learned Basis Adaptation for Parametric Linear PDEs
by: Dwivedi, Vikas, et al.
Published: (2026) -
Soft Partition-based KAPI-ELM for Multi-Scale PDEs
by: Dwivedi, Vikas, et al.
Published: (2026) -
Curriculum Learning-Driven PIELMs for Fluid Flow Simulations
by: Dwivedi, Vikas, et al.
Published: (2025) -
Distributed physics informed neural network for data-efficient solution to partial differential equations
by: Dwivedi, Vikas, et al.
Published: (2019)