Cover Image

A note on the physical interpretation of neural PDE's

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Succi, Sauro
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Disordered Systems and Neural Networks Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Evidence of Scaling Regimes in the Hopfield Dynamics of Whole Brain Model
by: Gosti, Giorgio, et al.
Published: (2024)

Hyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatz
by: Dao, H. L.
Published: (2025)

Pruning-induced phases in fully-connected neural networks: the eumentia, the dementia, and the amentia
by: Pan, Haining, et al.
Published: (2026)

Statistical physics analysis of graph neural networks: Approaching optimality in the contextual stochastic block model
by: Duranthon, O., et al.
Published: (2025)

Statistical physics for artificial neural networks
by: Pei, Zongrui
Published: (2025)

New non-Euclidean neural quantum states from additional types of hyperbolic recurrent neural networks
by: Dao, H. L.
Published: (2026)

On the interpretability of neural network decoders
by: Bödeker, Lukas, et al.
Published: (2025)

What can we learn from quantum convolutional neural networks?
by: Umeano, Chukwudubem, et al.
Published: (2023)

Emergent weight morphologies in deep neural networks
by: de Jong, Pascal, et al.
Published: (2025)

High-dimensional learning of narrow neural networks
by: Cui, Hugo
Published: (2024)

Universal characteristics of deep neural network loss surfaces from random matrix theory
by: Baskerville, Nicholas P, et al.
Published: (2022)

The committee machine: Computational to statistical gaps in learning a two-layers neural network
by: Aubin, Benjamin, et al.
Published: (2018)

Optimized Machine Learning Methods for Studying the Thermodynamic Behavior of Complex Spin Systems
by: Kapitan, Dmitrii, et al.
Published: (2025)

Spin-Dependent Graph Neural Network Potential for Magnetic Materials
by: Yu, Hongyu, et al.
Published: (2022)

Explaining the Machine Learning Solution of the Ising Model
by: Alamino, Roberto C.
Published: (2024)

Supervised and Unsupervised protocols for hetero-associative neural networks
by: Alessandrelli, Andrea, et al.
Published: (2025)

Computing frustration and near-monotonicity in deep neural networks
by: Wendin, Joel, et al.
Published: (2025)

Deep neural networks from the perspective of ergodic theory
by: Zhang, Fan
Published: (2023)

Finite-time Lyapunov exponents of deep neural networks
by: Storm, L., et al.
Published: (2023)

Training neural networks with structured noise improves classification and generalization
by: Benedetti, Marco, et al.
Published: (2023)

How does training shape the Riemannian geometry of neural network representations?
by: Zavatone-Veth, Jacob A., et al.
Published: (2023)

Training single-electron and single-photon stochastic physical neural networks
by: Dou, Tong, et al.
Published: (2026)

Towards a theory of how the structure of language is acquired by deep neural networks
by: Cagnetta, Francesco, et al.
Published: (2024)

Adaptive kernel predictors from feature-learning infinite limits of neural networks
by: Lauditi, Clarissa, et al.
Published: (2025)

Boundary between noise and information applied to filtering neural network weight matrices
by: Staats, Max, et al.
Published: (2022)

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

Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
by: D'Amico, Francesco, et al.
Published: (2025)

Properties of the geometry of solutions and capacity of multi-layer neural networks with Rectified Linear Units activations
by: Baldassi, Carlo, et al.
Published: (2019)

Solution space and storage capacity of fully connected two-layer neural networks with generic activation functions
by: Nishiyama, Sota, et al.
Published: (2024)

Sharp description of local minima in the loss landscape of high-dimensional two-layer ReLU neural networks
by: Huang, Jie, et al.
Published: (2026)

Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms
by: Srimahajariyapong, Kasidit, et al.
Published: (2025)

Role of scrambling and noise in temporal information processing with quantum systems
by: Xiong, Weijie, et al.
Published: (2025)

Polariton lattices as binarized neuromorphic networks
by: Sedov, Evgeny, et al.
Published: (2024)

Generative diffusion for perceptron problems: statistical physics analysis and efficient algorithms
by: Demyanenko, Elizaveta, et al.
Published: (2025)

Machine learning for sustainable geoenergy: uncertainty, physics and decision-ready inference
by: Menke, Hannah P., et al.
Published: (2026)

Recurrent Self-Attention Dynamics: An Energy-Agnostic Perspective from Jacobians
by: Tomihari, Akiyoshi, et al.
Published: (2025)

Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos
by: Sarmiento, Lucas Fernandez
Published: (2026)

Random Features Hopfield Networks generalize retrieval to previously unseen examples
by: Kalaj, Silvio, et al.
Published: (2024)

Thermal Robustness of Retrieval in Dense Associative Memories: LSE vs LSR Kernels
by: Petrova, Tatiana
Published: (2026)

Supervised Hebbian Learning
by: Alemanno, Francesco, et al.
Published: (2022)