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12/11/2022
Neural Continuous-Time Markov Models
Continuous-time Markov chains are used to model stochastic systems where...
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12/06/2022
Drift Identification for Lévy alpha-Stable Stochastic Systems
This paper focuses on a stochastic system identification problem: given ...
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12/14/2021
Dynamic Learning of Correlation Potentials for a Time-Dependent Kohn-Sham System
We develop methods to learn the correlation potential for a time-depende...
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08/14/2021
Equity-Directed Bootstrapping: Examples and Analysis
When faced with severely imbalanced binary classification problems, we o...
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07/31/2021
Statistical learning method for predicting density-matrix based electron dynamics
We develop a statistical method to learn a molecular Hamiltonian matrix ...
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12/06/2020
Estimating Vector Fields from Noisy Time Series
While there has been a surge of recent interest in learning differential...
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11/22/2020
A non-autonomous equation discovery method for time signal classification
Certain neural network architectures, in the infinite-layer limit, lead ...
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07/19/2020
Machine Learning a Molecular Hamiltonian for Predicting Electron Dynamics
We develop a mathematical method to learn a molecular Hamiltonian from m...
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07/26/2019
Learning and Interpreting Potentials for Classical Hamiltonian Systems
We consider the problem of learning an interpretable potential energy fu...
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10/16/2018
A Direct Method to Learn States and Parameters of Ordinary Differential Equations
Though ordinary differential equations (ODE) are used extensively in sci...
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11/28/2017
