A runtime-trained, uncertainty-driven ML model accelerates kinetic Monte Carlo simulations of atomistic thin-film growth while retaining fidelity to interatomic potentials.
Barnafi, Luca F
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The paper reviews recent developments and unresolved challenges in cardiac mechanics modeling, arguing that identifying essential complexities versus safe simplifications is key to clinical translation.
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A Self-Evolving Machine-Learning-Based Kinetic Monte Carlo Method for Modelling Thin-Film Growth
A runtime-trained, uncertainty-driven ML model accelerates kinetic Monte Carlo simulations of atomistic thin-film growth while retaining fidelity to interatomic potentials.
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Cardiac mechanics modeling: recent developments and current challenges
The paper reviews recent developments and unresolved challenges in cardiac mechanics modeling, arguing that identifying essential complexities versus safe simplifications is key to clinical translation.