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National Repository of Grey Literature 1 records found  Search took 0.00 seconds. 
Machine learning through geometric mechanics and thermodynamics
 Šípka, Martin ; Pavelka, Michal (advisor) ; Monmarché, Pierre (referee) ; Maršálek, Ondřej (referee)
30. prosinec 2023 This thesis studies novel approaches to learning of physical models, incorporat- ing constraints and optimizing path dependent loss functions. Recent advances in deep learning and artificial intelligence are connected with established knowl- edge about dynamical and chemical systems, offering new synergies and improv- ing upon existing methodologies. We present significant contributions to sim- ulation techniques that utilize automatic differentiation to propagate through the dynamics, showing not only their promising use case but also formulating new theoretical results about the gradient behavior in long evolutions controlled by neural networks. All the tools are carefully tested and evaluated on exam- ples from physics and chemistry, thus proposing and promoting their further applications. 1
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