:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Hmida, Hamda, Joly, Hsiu-Wen Chang, Mesri, Youssef
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2601.07384
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Compositional Neural Operators for Multi-Dimensional Fluid Dynamics
by: Hmida, Hamda, et al.
Published: (2026)

Towards a Foundation Model for Partial Differential Equations Across Physics Domains
by: Soares, Eduardo, et al.
Published: (2025)

Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation
by: Sun, Jingmin, et al.
Published: (2024)

A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations
by: Yang, Qihong, et al.
Published: (2026)

Particle-Guided Diffusion Models for Partial Differential Equations
by: Millard, Andrew, et al.
Published: (2026)

Foundation Inference Models for Ordinary Differential Equations
by: Mauel, Maximilian, et al.
Published: (2026)

Solving Partial Differential Equations with Random Feature Models
by: Liao, Chunyang
Published: (2024)

Latent Mamba Operator for Partial Differential Equations
by: Tiwari, Karn, et al.
Published: (2025)

Self-supervised Pretraining for Partial Differential Equations
by: Madhavan, Varun, et al.
Published: (2024)

Real-Time Machine Learning for Embedded Anomaly Detection
by: Benmachiche, Abdelmadjid, et al.
Published: (2025)

Base Models for Parabolic Partial Differential Equations
by: Xu, Xingzi, et al.
Published: (2024)

In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
by: Seifner, Patrick, et al.
Published: (2025)

Second-Order Tensorial Partial Differential Equations on Graphs
by: Einizade, Aref, et al.
Published: (2025)

Disentangled Representation Learning for Parametric Partial Differential Equations
by: Liu, Ning, et al.
Published: (2024)

Scale-Consistent Learning for Partial Differential Equations
by: Li, Zongyi, et al.
Published: (2025)

Layer Separation Deep Learning Model with Auxiliary Variables for Partial Differential Equations
by: Liu, Yaru, et al.
Published: (2025)

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs
by: Longhi, Alessandro, et al.
Published: (2025)

Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations
by: Shi, Dai, et al.
Published: (2026)

Multi-scale Time-stepping of Partial Differential Equations with Transformers
by: Hemmasian, AmirPouya, et al.
Published: (2023)

Solving Partial Differential Equations with Equivariant Extreme Learning Machines
by: Harder, Hans, et al.
Published: (2024)

Lie Generator Networks for Nonlinear Partial Differential Equations
by: Jamil, Shafayeth, et al.
Published: (2026)

FusionDP: Foundation Model-Assisted Differentially Private Learning for Partially Sensitive Features
by: Zeng, Linghui, et al.
Published: (2025)

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models
by: Chen, Xiangyu, et al.
Published: (2025)

DeepLeak: Privacy Enhancing Hardening of Model Explanations Against Membership Leakage
by: Hmida, Firas Ben, et al.
Published: (2026)

ConDiff: A Challenging Dataset for Neural Solvers of Partial Differential Equations
by: Trifonov, Vladislav, et al.
Published: (2024)

AE-ViT: Stable Long-Horizon Parametric Partial Differential Equations Modeling
by: Mikuš, Iva, et al.
Published: (2026)

WLNO: Wavelet-Laplace Neural Operator for Solving Partial Differential Equations
by: Abid, Muhammad, et al.
Published: (2026)

Physics-Informed Laplace Neural Operator for Solving Partial Differential Equations
by: Kim, Heechang, et al.
Published: (2026)

Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations
by: Ben-Yair, Ido, et al.
Published: (2021)

Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
by: Xiao, Xiongye, et al.
Published: (2023)

DDOT: A Derivative-directed Dual-decoder Ordinary Differential Equation Transformer for Dynamic System Modeling
by: Chang, Yang, et al.
Published: (2025)

Automating the Discovery of Partial Differential Equations in Dynamical Systems
by: Li, Weizhen, et al.
Published: (2024)

Learning to Solve Optimization Problems Constrained with Partial Differential Equations
by: Guven, Yusuf, et al.
Published: (2025)

HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
by: Bryutkin, Andrey, et al.
Published: (2024)

Physics-embedded Fourier Neural Network for Partial Differential Equations
by: Xu, Qingsong, et al.
Published: (2024)

Physics Informed Token Transformer for Solving Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2023)

Control and optimization for Neural Partial Differential Equations in Supervised Learning
by: Bensoussan, Alain, et al.
Published: (2025)

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
by: Mialon, Grégoire, et al.
Published: (2023)

Decoding Partial Differential Equations: Cross-Modal Adaptation of Decoder-only Models to PDEs
by: García-de-Herreros, Paloma, et al.
Published: (2025)

Dynamic Bayesian Optimization Framework for Instruction Tuning in Partial Differential Equation Discovery
by: Qu, Junqi, et al.
Published: (2025)