Zobrazeno 1 - 10
of 3 223
pro vyhledávání: '"Suryanarayana, P."'
Inferring dynamical models from data continues to be a significant challenge in computational biology, especially given the stochastic nature of many biological processes. We explore a common scenario in omics, where statistically independent cross-s
Externí odkaz:
http://arxiv.org/abs/2410.07501
We present a formalism for developing cyclic and helical symmetry-informed machine learned force fields (MLFFs). In particular, employing the smooth overlap of atomic positions descriptors with the polynomial kernel method, we derive cyclic and helic
Externí odkaz:
http://arxiv.org/abs/2408.07554
We present a framework for computing the shock Hugoniot using on-the-fly machine learned force field (MLFF) molecular dynamics simulations. In particular, we employ an MLFF model based on the kernel method and Bayesian linear regression to compute th
Externí odkaz:
http://arxiv.org/abs/2407.15290
Autor:
Jing, Xin, Suryanarayana, Phanish
We present an efficient real space formalism for hybrid exchange-correlation functionals in generalized Kohn-Sham density functional theory (DFT). In particular, we develop an efficient representation for any function of the real space finite-differe
Externí odkaz:
http://arxiv.org/abs/2406.16998
We present a spectral scheme for atomic structure calculations in pseudopotential Kohn-Sham density functional theory. In particular, after applying an exponential transformation of the radial coordinates, we employ global polynomial interpolation on
Externí odkaz:
http://arxiv.org/abs/2406.00534
We propose mixed boundary conditions for 3d conformal gravity consistent with variational principle in its second-order formalism that admit the chiral $\Lambda$-$\mathfrak{bms}_4$ algebra as their asymptotic symmetry algebra. This algebra is one of
Externí odkaz:
http://arxiv.org/abs/2405.20244
We develop a framework for on-the-fly machine learned force field molecular dynamics simulations based on the multipole featurization scheme that overcomes the bottleneck with the number of chemical elements. Considering bulk systems with up to 6 ele
Externí odkaz:
http://arxiv.org/abs/2404.07961
We develop a framework for on-the-fly machine learned force field (MLFF) molecular dynamics (MD) simulations of warm dense matter (WDM). In particular, we employ an MLFF scheme based on the kernel method and Bayesian linear regression, with the train
Externí odkaz:
http://arxiv.org/abs/2402.13450
Inferring causation from time series data is of scientific interest in different disciplines, particularly in neural connectomics. While different approaches exist in the literature with parametric modeling assumptions, we focus on a non-parametric m
Externí odkaz:
http://arxiv.org/abs/2312.09604
Autor:
Wang, Yu, Yin, Yuxuan, Suryanarayana, Karthik Somayaji Nanjangud, Drgona, Jan, Schram, Malachi, Halappanavar, Mahantesh, Liu, Frank, Li, Peng
Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Recently, data-driven approaches such as Neural Ordinary Differential Equ
Externí odkaz:
http://arxiv.org/abs/2310.13110