:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Niarchos, Vasilis, Sirbu, Angelos, Trifinopoulos, Sokratis
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Artificial Intelligence Numerical Analysis Nuclear Theory
Accès en ligne:	https://arxiv.org/abs/2605.07792
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

The DNA of nuclear models: How AI predicts nuclear masses
par: Richardson, Kate A., et autres
Publié: (2025)

When Does Critique Improve AI-Assisted Theoretical Physics? SCALAR: Structured Critic--Actor Loop for Agentic Reasoning
par: Niarchos, Vasilis, et autres
Publié: (2026)

From Neurons to Neutrons: A Case Study in Interpretability
par: Kitouni, Ouail, et autres
Publié: (2024)

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
par: Berner, Julius, et autres
Publié: (2025)

CATO: Charted Attention for Neural PDE Operators
par: Cheng, Chun-Wun, et autres
Publié: (2026)

Neural Operators with Localized Integral and Differential Kernels
par: Liu-Schiaffini, Miguel, et autres
Publié: (2024)

Autoregression-Free Neural Operators for Time-Dependent PDEs
par: Zhang, Jiaquan, et autres
Publié: (2026)

Monte Carlo-Type Neural Operator for Differential Equations
par: Choutri, Salah Eddine, et autres
Publié: (2025)

FI-KAN: Fractal Interpolation Kolmogorov-Arnold Networks
par: N'guessan, Gnankan Landry Regis
Publié: (2026)

Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
par: Wang, Hong, et autres
Publié: (2024)

Data-Efficient Neural Operator Training via Physics-Based Active Learning
par: Polanska, Alicja, et autres
Publié: (2026)

Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation
par: Singh, Ashutosh, et autres
Publié: (2024)

Beyond Loss Guidance: Using PDE Residuals as Spectral Attention in Diffusion Neural Operators
par: Sawhney, Medha, et autres
Publié: (2025)

CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
par: Hou, Xianglong, et autres
Publié: (2025)

Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study
par: Patel, Janak M., et autres
Publié: (2025)

Operator learning without the adjoint
par: Boullé, Nicolas, et autres
Publié: (2024)

Graph Neural Network as Computationally Efficient Emulator of Ice-sheet and Sea-level System Model (ISSM)
par: Koo, Younghyun, et autres
Publié: (2024)

A Mathematical Guide to Operator Learning
par: Boullé, Nicolas, et autres
Publié: (2023)

Characteristic Learning for Provable One Step Generation
par: Ding, Zhao, et autres
Publié: (2024)

Why Does Stochastic Gradient Descent Slow Down in Low-Precision Training?
par: Yun, Vincent-Daniel
Publié: (2025)

Projection Methods for Operator Learning and Universal Approximation
par: Zappala, Emanuele
Publié: (2024)

Mixture of Experts Softens the Curse of Dimensionality in Operator Learning
par: Kratsios, Anastasis, et autres
Publié: (2024)

STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problem
par: Wang, Hong, et autres
Publié: (2025)

Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
par: Zhang, Ce, et autres
Publié: (2023)

Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data
par: Fukushima, Kenji, et autres
Publié: (2025)

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
par: Son, Hwijae
Publié: (2025)

PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws
par: Yang, Liu, et autres
Publié: (2024)

Stability and Discretization Error of State Space Model Neural Operators
par: Bendahi, Abderrahim, et autres
Publié: (2026)

Quasi-Random Physics-informed Neural Networks
par: Yu, Tianchi, et autres
Publié: (2025)

Guided Diffusion Sampling on Function Spaces with Applications to PDEs
par: Yao, Jiachen, et autres
Publié: (2025)

Spectral Informed Neural Network: An Efficient and Low-Memory PINN
par: Yu, Tianchi, et autres
Publié: (2024)

UGrid: An Efficient-And-Rigorous Neural Multigrid Solver for Linear PDEs
par: Han, Xi, et autres
Publié: (2024)

Huber-energy measure quantization
par: Turinici, Gabriel
Publié: (2022)

Quantum Neural Network Restatement of Markov Jump Process
par: Zarezadeh, Z., et autres
Publié: (2025)

A Dimensionality Reduction Approach for Convolutional Neural Networks
par: Meneghetti, Laura, et autres
Publié: (2021)

Learning from Integral Losses in Physics Informed Neural Networks
par: Saleh, Ehsan, et autres
Publié: (2023)

Graph-Instructed Neural Networks for parametric problems with varying boundary conditions
par: Della Santa, Francesco, et autres
Publié: (2026)

Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers
par: Zhou, Hang, et autres
Publié: (2024)

Solving PDEs With Deep Neural Nets under General Boundary Conditions
par: Zhang, Chenggong
Publié: (2025)

Better Neural PDE Solvers Through Data-Free Mesh Movers
par: Hu, Peiyan, et autres
Publié: (2023)