:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Sliheet, Elyssa, Talha, Md Abu, Geng, Weihua
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Mathematical Physics
Online Access:	https://arxiv.org/abs/2409.03658
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
by: Menon, Sidharth S., et al.
Published: (2026)

Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data
by: Geng, Ru, et al.
Published: (2026)

Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
by: Basterrech, Sebastian, et al.
Published: (2025)

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling
by: Zang, Yaohua, et al.
Published: (2025)

A regularized Matched Interface and Boundary Method (MIB) for Solving Polarizable Multipole Poisson-Boltzmann model
by: Yang, Xin, et al.
Published: (2025)

Score Reversal Is Not Free for Quantum Diffusion Models
by: Fayad, Ammar
Published: (2026)

Uncertainty Quantification for Machine Learning-Based Prediction: A Polynomial Chaos Expansion Approach for Joint Model and Input Uncertainty Propagation
by: Du, Xiaoping
Published: (2025)

A Learning-based Domain Decomposition Method
by: Wu, Rui, et al.
Published: (2025)

A review of graph neural network applications in mechanics-related domains
by: Zhao, Yingxue, et al.
Published: (2024)

Deep Neural Networks as the Semi-classical Limit of Topological Quantum Neural Networks: The problem of generalisation
by: Marciano, Antonino, et al.
Published: (2022)

Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling
by: Propp, Adrienne M., et al.
Published: (2024)

Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law
by: Zhang, Jingdong, et al.
Published: (2024)

BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows $-$ a case study in optimal monitor well placement for CO$_2$ sequestration
by: Orozco, Rafael, et al.
Published: (2024)

Loss Jump During Loss Switch in Solving PDEs with Neural Networks
by: Wang, Zhiwei, et al.
Published: (2024)

Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations
by: Meng, Bin, et al.
Published: (2024)

ASPINN: An asymptotic strategy for solving singularly perturbed differential equations
by: Wang, Sen, et al.
Published: (2024)

Neumann Series-based Neural Operator for Solving Inverse Medium Problem
by: Liu, Ziyang, et al.
Published: (2024)

Physics-Informed Deep Learning of Rate-and-State Fault Friction
by: Rucker, Cody, et al.
Published: (2023)

Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments
by: Mishra, Aashwin, et al.
Published: (2026)

Learning Heat-based Equations in Self-similar variables
by: Wang, Shihao, et al.
Published: (2026)

Efficient Error Certification for Physics-Informed Neural Networks
by: Eiras, Francisco, et al.
Published: (2023)

Physics-informed neural networks to solve inverse problems in unbounded domains
by: Pérez-Bernal, Gregorio, et al.
Published: (2025)

Generative Path-Finding Method for Wasserstein Gradient Flow
by: Liu, Chengyu, et al.
Published: (2026)

Symbolic Recovery of Differential Equations: The Identifiability Problem
by: Scholl, Philipp, et al.
Published: (2022)

Symbolic Neural Ordinary Differential Equations
by: Li, Xin, et al.
Published: (2025)

Symbolic identification of tensor equations in multidimensional physical fields
by: Chen, Tianyi, et al.
Published: (2025)

RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles
by: Behnoudfar, Pouria, et al.
Published: (2025)

PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure
by: Wu, Junyi, et al.
Published: (2025)

Continual Learning as a Multiphase Moving-Boundary Problem
by: Khilar, Snigdha Chandan
Published: (2026)

Frequency-adaptive tensor neural networks for high-dimensional multi-scale problems
by: Huang, Jizu, et al.
Published: (2025)

A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations
by: Gupta, Naveen, et al.
Published: (2024)

Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates
by: Bora, Aniruddha, et al.
Published: (2025)

A Topological Approach to Parameterizing Deep Hedging Networks
by: Das, Alok, et al.
Published: (2025)

A Matrix Product State Model for Simultaneous Classification and Generation
by: Mossi, Alex, et al.
Published: (2024)

Few measurement shots challenge generalization in learning to classify entanglement
by: Banchi, Leonardo, et al.
Published: (2024)

H-EFT-VA: An Effective-Field-Theory Variational Ansatz with Provable Barren Plateau Avoidance
by: Hamid, Eyad I. B
Published: (2026)

Random-Matrix-Induced Simplicity Bias in Over-parameterized Variational Quantum Circuits
by: Qi, Jun, et al.
Published: (2026)

Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets
by: Propp, Adrienne M., et al.
Published: (2025)

Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?
by: Kim, Tae-Geun, et al.
Published: (2024)

Grokking in Linear Estimators -- A Solvable Model that Groks without Understanding
by: Levi, Noam, et al.
Published: (2023)