44 citations · 50 across the 5 of their papers we have counts for
6 papers
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional
Sanjeev Raja, Martin Šípka, Michael Psenka +3
Transition path sampling (TPS), which involves finding probable paths connecting two points on an energy landscape, remains a challenge due to the complexity of real-world atomisti…
A reactive neural network framework for water-loaded acidic zeolites
Andreas Erlebach, Martin Šípka, Indranil Saha +3
Under operating conditions, the dynamics of water and ions confined within protonic aluminosilicate zeolite micropores are responsible for many of their properties, including hydro…
Direct Poisson neural networks: Learning non-symplectic mechanical systems
Martin Šípka, Michal Pavelka, Oğul Esen +1
In this paper, we present neural networks learning mechanical systems that are both symplectic (for instance particle mechanics) and non-symplectic (for instance rotating rigid bod…
Differentiable Simulations for Enhanced Sampling of Rare Events
Martin Šípka, Johannes C. B. Dietschreit, Lukáš Grajciar +1
Simulating rare events, such as the transformation of a reactant into a product in a chemical reaction typically requires enhanced sampling techniques that rely on heuristically ch…
On compatibility of the Natural configuration framework with GENERIC: Derivation of anisotropic rate-type models
Petr Pelech, Karel Tuma, Michal Pavelka +2
Within the framework of natural configurations developed by Rajagopal and Srinivasa, evolution within continuum thermodynamics is formulated as evolution of a natural configuration…
Learning Physics from Data: a Thermodynamic Interpretation
Francisco Chinesta, Elias Cueto, Miroslav Grmela +3
Experimental data bases are typically very large and high dimensional. To learn from them requires to recognize important features (a pattern), often present at scales different to…