5 papers
Stochastic Approximation and Brownian Repulsion based Evolutionary Search
Rajdeep Dutta, T Venkatesh Varma, Saikat Sarkar +4
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 po…
FINDER: Stochastic Mirroring of Noisy Quasi-Newton Search and Deep Network Training
Uttam Suman, Mariya Mamajiwala, Mukul Saxena +2
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 ha…
Physics Informed Deep Learning for Strain Gradient Continuum Plasticity
Ankit Tyagi, Uttam Suman, Mariya Mamajiwala +1
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 diffe…
Geometrically adapted Langevin dynamics for Markov chain Monte Carlo simulations
Mariya Mamajiwala, Debasish Roy, Serge Guillas
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…
Stochastic dynamical systems developed on Riemannian manifolds
Mariya Mamajiwala, Debasish Roy
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 suitab…