Copertina

Deep generative modelling of canonical ensemble with differentiable thermal properties

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Shuo-Hui, Zhang, Yao-Wen, Pan, Ding
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Statistical Mechanics Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Differentiable free energy surface: a variational approach to directly observing rare events using generative deep-learning models
di: Li, Shuo-Hui, et al.
Pubblicazione: (2026)

A differentiable programming framework for spin models
di: Farias, Tiago de Souza, et al.
Pubblicazione: (2023)

Plastic tensor networks for interpretable generative modeling
di: Akamatsu, Katsuya O., et al.
Pubblicazione: (2025)

Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach
di: Braghetto, Anna, et al.
Pubblicazione: (2023)

Discrete generative diffusion models without stochastic differential equations: a tensor network approach
di: Causer, Luke, et al.
Pubblicazione: (2024)

Composing diffusion priors with explicit physical context via generative Gibbs sampling
di: Wang, Weizhou, et al.
Pubblicazione: (2026)

Data driven modeling for self-similar dynamics
di: Tao, Ruyi, et al.
Pubblicazione: (2023)

SETOL: A Semi-Empirical Theory of (Deep) Learning
di: Martin, Charles H, et al.
Pubblicazione: (2025)

Accurate generation of stochastic dynamics based on multi-model Generative Adversarial Networks
di: Lanzoni, Daniele, et al.
Pubblicazione: (2023)

Expanding functional protein sequence space using high entropy generative models
di: Netti, Roberto, et al.
Pubblicazione: (2026)

An effective potential for generative modelling with active matter
di: Baule, Adrian
Pubblicazione: (2025)

Learning conformational ensembles of proteins based on backbone geometry
di: Wolf, Nicolas, et al.
Pubblicazione: (2025)

CoRMF: Criticality-Ordered Recurrent Mean Field Ising Solver
di: Pan, Zhenyu, et al.
Pubblicazione: (2024)

Computationally sufficient statistics for Ising models
di: Jayakumar, Abhijith, et al.
Pubblicazione: (2026)

Learning the Intrinsic Dimensionality of Fermi-Pasta-Ulam-Tsingou Trajectories: A Nonlinear Approach using a Deep Autoencoder Model
di: Marchetti, Gionni
Pubblicazione: (2026)

Polyformer: a generative framework for thermodynamic modeling of polymeric molecules
di: Valentini, Alessio, et al.
Pubblicazione: (2026)

Speed-accuracy relations for diffusion models: Wisdom from nonequilibrium thermodynamics and optimal transport
di: Ikeda, Kotaro, et al.
Pubblicazione: (2024)

Nonequilbrium physics of generative diffusion models
di: Yu, Zhendong, et al.
Pubblicazione: (2024)

Memorisation, convergence and generalisation in generative models
di: Maillard, Antoine, et al.
Pubblicazione: (2026)

Machine learning for structure-property relationships: Scalability and limitations
di: Tian, Zhongzheng, et al.
Pubblicazione: (2023)

Pointwise Generalization in Deep Neural Networks
di: Li, Shaojie, et al.
Pubblicazione: (2026)

High-entropy Advantage in Neural Networks' Generalizability
di: Yang, Entao, et al.
Pubblicazione: (2025)

Fermi-Dirac thermal measurements: A framework for quantum hypothesis testing and semidefinite optimization
di: Liu, Nana, et al.
Pubblicazione: (2026)

A new pathway to generative artificial intelligence by minimizing the maximum entropy
di: Miotto, Mattia, et al.
Pubblicazione: (2025)

Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
di: Egenlauf, Patrick, et al.
Pubblicazione: (2025)

Concurrence of Symmetry Breaking and Nonlocality Phase Transitions in Diffusion Models
di: Zhang, Yifan F., et al.
Pubblicazione: (2026)

A renormalization-group inspired lattice-based framework for piecewise generalized linear models
di: Chang, Joshua C.
Pubblicazione: (2026)

Assessing generative modeling approaches for free energy estimates in condensed matter
di: Schebek, Maximilian, et al.
Pubblicazione: (2025)

Identifying Ising and percolation phase transitions based on KAN method
di: Xu, Dian, et al.
Pubblicazione: (2025)

Identifying percolation phase transitions with unsupervised learning based on largest clusters
di: Xu, Dian, et al.
Pubblicazione: (2023)

Bose-Einstein condensation in canonical ensemble with fixed total momentum
di: Plyashechnik, Andrey S., et al.
Pubblicazione: (2024)

Tensor tree learns hidden relational structures in data to construct generative models
di: Harada, Kenji, et al.
Pubblicazione: (2024)

Physics Informed Deep Learning for Strain Gradient Continuum Plasticity
di: Tyagi, Ankit, et al.
Pubblicazione: (2024)

Description using equilibrium temperature in the canonical ensemble within the framework of the Tsallis statistics employing the conventional expectation value
di: Ishihara, Masamichi
Pubblicazione: (2025)

Review and Prospect of Algebraic Research in Equivalent Framework between Statistical Mechanics and Machine Learning Theory
di: Watanabe, Sumio
Pubblicazione: (2024)

Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning
di: Elliott, Chris, et al.
Pubblicazione: (2026)

Statistics of Min-max Normalized Eigenvalues in Random Matrices
di: Nakada, Hyakka, et al.
Pubblicazione: (2025)

Laws of thermodynamics for exponential families
di: Balsubramani, Akshay
Pubblicazione: (2025)

Dynamical Regimes of Diffusion Models
di: Biroli, Giulio, et al.
Pubblicazione: (2024)

Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models
di: Máté, Bálint, et al.
Pubblicazione: (2024)