:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Monsel, Thibault, Semeraro, Onofrio, Mathelin, Lionel, Charpiat, Guillaume
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence Computational Physics
Accesso online:	https://arxiv.org/abs/2410.02843
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Time and State Dependent Neural Delay Differential Equations
di: Monsel, Thibault, et al.
Pubblicazione: (2023)

Learning non-Markovian Dynamical Systems with Signature-based Encoders
di: Pradeleix, Eliott, et al.
Pubblicazione: (2025)

Evidence on the Regularisation Properties of Maximum-Entropy Reinforcement Learning
di: Hosseinkhan-Boucher, Rémy, et al.
Pubblicazione: (2025)

Increasing Information for Model Predictive Control with Semi-Markov Decision Processes
di: Hosseinkhan-Boucher, Rémy, et al.
Pubblicazione: (2025)

An Implicit GNN Solver for Poisson-like problems
di: Nastorg, Matthieu, et al.
Pubblicazione: (2023)

General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
di: Ma, Genwei, et al.
Pubblicazione: (2026)

Breakeven complexity: A new perspective on neural partial differential equation solvers
di: Zhang, Yijing, et al.
Pubblicazione: (2026)

Growth strategies for arbitrary DAG neural architectures
di: Douka, Stella, et al.
Pubblicazione: (2025)

Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
di: Li, Zijie, et al.
Pubblicazione: (2024)

Invertible Koopman neural operator for data-driven modeling of partial differential equations
di: Jin, Yuhong, et al.
Pubblicazione: (2025)

Completion of partial structures using Patterson maps with the CrysFormer machine learning model
di: Pan, Tom, et al.
Pubblicazione: (2025)

Context selectivity with dynamic availability enables lifelong continual learning
di: Barry, Martin, et al.
Pubblicazione: (2023)

Dynamic Gaussian Graph Operator: Learning parametric partial differential equations in arbitrary discrete mechanics problems
di: Wang, Chu, et al.
Pubblicazione: (2024)

The power and limitations of learning quantum dynamics incoherently
di: Jerbi, Sofiene, et al.
Pubblicazione: (2023)

Extending Causal Metamodeling to a non-Markovian Queue
di: Amaranath, Pracheta, et al.
Pubblicazione: (2026)

Physically consistent and uncertainty-aware learning of spatiotemporal dynamics
di: Xu, Qingsong, et al.
Pubblicazione: (2025)

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

Physics-informed fine-tuning of foundation models for partial differential equations
di: Medvedev, Vlad, et al.
Pubblicazione: (2026)

A hybrid numerical methodology coupling Reduced Order Modeling and Graph Neural Networks for non-parametric geometries: applications to structural dynamics problems
di: Matray, Victor, et al.
Pubblicazione: (2024)

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

PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks
di: Almanstötter, Marius, et al.
Pubblicazione: (2025)

Z-Error Loss for Training Neural Networks
di: Godin, Guillaume
Pubblicazione: (2025)

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

Forager: a lightweight testbed for continual learning with partial observability in RL
di: Tang, Steven, et al.
Pubblicazione: (2026)

arcjetCV: an open-source software to analyze material ablation
di: Quintart, Alexandre, et al.
Pubblicazione: (2024)

Learning noisy phase transition dynamics from stochastic partial differential equations
di: Sun, Luning, et al.
Pubblicazione: (2026)

Scientific machine learning in ecological systems: A study on the predator-prey dynamics
di: Devgupta, Ranabir, et al.
Pubblicazione: (2024)

Preventing overfitting in deep learning using differential privacy
di: Khatri, Alizishaan Anwar Hussein
Pubblicazione: (2026)

Enhanced Transformer architecture for in-context learning of dynamical systems
di: Rufolo, Matteo, et al.
Pubblicazione: (2024)

LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
di: Liu, Toni J. B., et al.
Pubblicazione: (2024)

Discover physical concepts and equations with machine learning
di: Li, Bao-Bing, et al.
Pubblicazione: (2024)

Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
di: Kundu, Akash, et al.
Pubblicazione: (2024)

Delayed homomorphic reinforcement learning for environments with delayed feedback
di: Lee, Jongsoo, et al.
Pubblicazione: (2026)

Policy Dispersion in Non-Markovian Environment
di: Qu, Bohao, et al.
Pubblicazione: (2023)

Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics
di: Komatsu, Hisato
Pubblicazione: (2023)

SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
di: Lalou, Yanis, et al.
Pubblicazione: (2024)

Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme
di: Morel, Rudy, et al.
Pubblicazione: (2025)

Shadows of quantum machine learning
di: Jerbi, Sofiene, et al.
Pubblicazione: (2023)

Does learning the right latent variables necessarily improve in-context learning?
di: Mittal, Sarthak, et al.
Pubblicazione: (2024)

Neural ocean forecasting from sparse satellite-derived observations: a case-study for SSH dynamics and altimetry data
di: Botvynko, Daria, et al.
Pubblicazione: (2025)