:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Zimmer, Michael F.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2404.02896
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Constants of Motion for Conserved and Non-conserved Dynamics
by: Zimmer, Michael F.
Published: (2024)

Extracting Dynamical Models from Data
by: Zimmer, Michael F.
Published: (2021)

Structure-preserving Lift & Learn: Scientific machine learning for nonlinear conservative partial differential equations
by: Sharma, Harsh, et al.
Published: (2025)

Discretization-independent multifidelity operator learning for partial differential equations
by: Hauck, Jacob, et al.
Published: (2025)

Reinforcement learning-based estimation for partial differential equations
by: Mowlavi, Saviz, et al.
Published: (2023)

Electron flow matching for generative reaction mechanism prediction obeying conservation laws
by: Joung, Joonyoung F., et al.
Published: (2025)

Learning by solving differential equations
by: Dherin, Benoit, et al.
Published: (2025)

Integration of physics-informed operator learning and finite element method for parametric learning of partial differential equations
by: Rezaei, Shahed, et al.
Published: (2024)

Pseudo-Hamiltonian neural networks for learning partial differential equations
by: Eidnes, Sølve, et al.
Published: (2023)

One-shot learning for solution operators of partial differential equations
by: Jiao, Anran, et al.
Published: (2021)

Active operator learning with predictive uncertainty quantification for partial differential equations
by: Winovich, Nick, et al.
Published: (2025)

Ricci flow regularization in latent spaces for the forward learning of partial differential equations
by: Gracyk, Andrew
Published: (2024)

Neural equilibria for long-term prediction of nonlinear conservation laws
by: Benitez, J. Antonio Lara, et al.
Published: (2025)

On the Robustness of Distributed Machine Learning against Transfer Attacks
by: Andreina, Sébastien, et al.
Published: (2024)

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics
by: Liu, Xin-Yang, et al.
Published: (2022)

Deep learning based numerical approximation algorithms for stochastic partial differential equations
by: Beck, Christian, et al.
Published: (2020)

Comparing statistical and deep learning techniques for parameter estimation of continuous-time stochastic differentiable equations
by: Sankoh, Aroon, et al.
Published: (2025)

Federated scientific machine learning for approximating functions and solving differential equations with data heterogeneity
by: Zhang, Handi, et al.
Published: (2024)

Estimating unknown parameters in differential equations with a reinforcement learning based PSO method
by: Sun, Wenkui, et al.
Published: (2024)

Bridging quantum and classical computing for partial differential equations through multifidelity machine learning
by: Jacob, Bruno, et al.
Published: (2025)

Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
by: Zhang, Benjamin J., et al.
Published: (2025)

The Price equation reveals a universal force-metric-bias law of algorithmic learning and natural selection
by: Frank, Steven A.
Published: (2025)

DynGMA: a robust approach for learning stochastic differential equations from data
by: Zhu, Aiqing, et al.
Published: (2024)

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

Towards true discovery of the differential equations
by: Hvatov, Alexander, et al.
Published: (2023)

Neural Entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws
by: Liu, Lizuo, et al.
Published: (2025)

Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?
by: Mundinger, Konrad, et al.
Published: (2025)

Method of data forward generation with partial differential equations for machine learning modeling in fluid mechanics
by: Chen, Ruilin
Published: (2025)

The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning
by: Zimmer, Max, et al.
Published: (2026)

Scaling laws for learning with real and surrogate data
by: Jain, Ayush, et al.
Published: (2024)

Physics-constrained robust learning of open-form partial differential equations from limited and noisy data
by: Du, Mengge, et al.
Published: (2023)

Optimal scaling laws in learning hierarchical multi-index models
by: Defilippis, Leonardo, et al.
Published: (2026)

Emergence and scaling laws in SGD learning of shallow neural networks
by: Ren, Yunwei, et al.
Published: (2025)

A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
by: Yamazaki, Yusuke, et al.
Published: (2024)

Symmetry-regularized neural ordinary differential equations
by: Hao, Wenbo
Published: (2023)

Distribution learning via neural differential equations: minimal energy regularization and approximation theory
by: Marzouk, Youssef, et al.
Published: (2025)

A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations
by: Nidhan, Sheel, et al.
Published: (2024)

Operator learning for hyperbolic partial differential equations
by: Wang, Christopher, et al.
Published: (2023)

Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
by: Xu, Tengfei, et al.
Published: (2023)

Neural delay differential equations: learning non-Markovian closures for partially known dynamical systems
by: Monsel, Thibault, et al.
Published: (2024)