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Numerical computation of rare events via large deviation theory
Tobias Grafke, Eric Vanden-Eijnden
An overview of rare events algorithms based on large deviation theory (LDT) is presented. It covers a range of numerical schemes to compute the large deviation minimizer in various…
Dynamical computation of the density of states and Bayes factors using nonequilibrium importance sampling
Grant M. Rotskoff, Eric Vanden-Eijnden
Nonequilibrium sampling is potentially much more versatile than its equilibrium counterpart, but it comes with challenges because the invariant distribution is not typically known…
Simulated Tempering Method in the Infinite Switch Limit with Adaptive Weight Learning
Anton Martinsson, Jianfeng Lu, Benedict Leimkuhler +1
We investigate the theoretical foundations of the simulated tempering method and use our findings to design efficient algorithms. Employing a large deviation argument first used fo…
Extreme event quantification in dynamical systems with random components
Giovanni Dematteis, Tobias Grafke, Eric Vanden-Eijnden
A central problem in uncertainty quantification is how to characterize the impact that our incomplete knowledge about models has on the predictions we make from them. This question…
Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach
Grant M. Rotskoff, Eric Vanden-Eijnden
Neural networks, a central tool in machine learning, have demonstrated remarkable, high fidelity performance on image recognition and classification tasks. These successes evince a…