Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Bhat, Harish S."'
We propose a framework to learn the time-dependent Hartree-Fock (TDHF) inter-electronic potential of a molecule from its electron density dynamics. Though the entire TDHF Hamiltonian, including the inter-electronic potential, can be computed from fir
Externí odkaz:
http://arxiv.org/abs/2408.04765
For any linear system where the unreduced dynamics are governed by unitary propagators, we derive a closed, time-delayed, linear system for a reduced-dimensional quantity of interest. We apply this method to understand the memory-dependence of $1$-el
Externí odkaz:
http://arxiv.org/abs/2403.15596
Autor:
Reeves, Majerle, Bhat, Harish S.
Continuous-time Markov chains are used to model stochastic systems where transitions can occur at irregular times, e.g., birth-death processes, chemical reaction networks, population dynamics, and gene regulatory networks. We develop a method to lear
Externí odkaz:
http://arxiv.org/abs/2212.05378
Autor:
Bhat, Harish S.
This paper focuses on a stochastic system identification problem: given time series observations of a stochastic differential equation (SDE) driven by L\'{e}vy $\alpha$-stable noise, estimate the SDE's drift field. For $\alpha$ in the interval $[1,2)
Externí odkaz:
http://arxiv.org/abs/2212.03317
Publikováno v:
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:546-558, 2022
We develop methods to learn the correlation potential for a time-dependent Kohn-Sham (TDKS) system in one spatial dimension. We start from a low-dimensional two-electron system for which we can numerically solve the time-dependent Schr\"odinger equat
Externí odkaz:
http://arxiv.org/abs/2112.07067
When faced with severely imbalanced binary classification problems, we often train models on bootstrapped data in which the number of instances of each class occur in a more favorable ratio, e.g., one. We view algorithmic inequity through the lens of
Externí odkaz:
http://arxiv.org/abs/2108.06624
We develop a statistical method to learn a molecular Hamiltonian matrix from a time-series of electron density matrices. We extend our previous method to larger molecular systems by incorporating physical properties to reduce dimensionality, while al
Externí odkaz:
http://arxiv.org/abs/2108.00318
While there has been a surge of recent interest in learning differential equation models from time series, methods in this area typically cannot cope with highly noisy data. We break this problem into two parts: (i) approximating the unknown vector f
Externí odkaz:
http://arxiv.org/abs/2012.03199
Certain neural network architectures, in the infinite-layer limit, lead to systems of nonlinear differential equations. Motivated by this idea, we develop a framework for analyzing time signals based on non-autonomous dynamical equations. We view the
Externí odkaz:
http://arxiv.org/abs/2011.11096
We develop a computational method to learn a molecular Hamiltonian matrix from matrix-valued time series of the electron density. As we demonstrate for three small molecules, the resulting Hamiltonians can be used for electron density evolution, prod
Externí odkaz:
http://arxiv.org/abs/2007.09814