Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Mamajiwala, Mariya"'
Autor:
Dutta, Rajdeep, Varma, T Venkatesh, Sarkar, Saikat, Mamajiwala, Mariya, Awad, Noor, Jayavelu, Senthilnath, Roy, Debasish
Many global optimization algorithms of the memetic variety rely on some form of stochastic search, and yet they often lack a sound probabilistic basis. Without a recourse to the powerful tools of stochastic calculus, treading the fine balance between
Externí odkaz:
http://arxiv.org/abs/2412.11036
Our proposal is on a new stochastic optimizer for non-convex and possibly non-smooth objective functions typically defined over large dimensional design spaces. Towards this, we have tried to bridge noise-assisted global search and faster local conve
Externí odkaz:
http://arxiv.org/abs/2410.14270
We use a space-time discretization based on physics informed deep learning (PIDL) to approximate solutions of a class of rate-dependent strain gradient plasticity models. The differential equation governing the plastic flow, the so-called microforce
Externí odkaz:
http://arxiv.org/abs/2408.06657
Markov Chain Monte Carlo (MCMC) is one of the most powerful methods to sample from a given probability distribution, of which the Metropolis Adjusted Langevin Algorithm (MALA) is a variant wherein the gradient of the distribution is used towards fast
Externí odkaz:
http://arxiv.org/abs/2201.08072
Autor:
Mamajiwala, Mariya, Roy, Debasish
We propose a method for developing the flows of stochastic dynamical systems, posed as Ito's stochastic differential equations, on a Riemannian manifold identified through a suitably constructed metric. The framework used for the stochastic developme
Externí odkaz:
http://arxiv.org/abs/2007.11927
Autor:
Mamajiwala, Mariya, Roy, Debasish
Publikováno v:
In International Journal of Mechanical Sciences 15 February 2022 216
Autor:
Mamajiwala, Mariya, Roy, Debasish
Publikováno v:
In Probabilistic Engineering Mechanics January 2022 67