Machine-Learning-Based Sampling in Lattice Field Theory and Quantum Chemistry

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Registration and Coffee

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Contributions*

A dynamical systems perspective on measure transport and generative modeling

Bethe Colloquium: Generative flow models for lattice field theory

Closing Remarks

Continuous normalizing flows for lattice gauge theories

Continuous Normalizing Flows in Lattice Gauge Theories

Hands on NeuLat: a toolbox for neural samplers in lattice field theory

Importance weights distribution in Neural Samplers

Keynote: Machine Learning and Chemistry

Keynote: Machine Learning and Generative Models

Keynote: Machine Learning and Physics

Modeling Biomolecular Structures through Generative Models

NETS: A non-equilibrium transport sampler

Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models

Normalizing flows for lattice gauge theory in 3+1D

Panel Discussion: Machine Learning and Chemistry

Panel Discussion: Machine Learning and Physics

Panel Discussion: Research in Industry and Academia

Poster Flashtalks (1 min pp)

Poster Session

Practical applications of machine-learned flows on gauge fields

Stochastic Normalizing Flows for lattice gauge theory

Thermodynamic Integration along interpolant ML-potentials

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

TRA Colloquium: Symmetries in AI4Science

Welcome Remarks

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Events scheduled on*

21/10/2024

22/10/2024

23/10/2024

24/10/2024

25/10/2024

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