Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Mukherjee, Amartya"'
This paper presents a novel approach to generating stabilizing controllers for a large class of dynamical systems using diffusion models. The core objective is to develop stabilizing control functions by identifying the closest asymptotically stable
Externí odkaz:
http://arxiv.org/abs/2403.17692
Autor:
Meng, Yiming, Zhou, Ruikun, Mukherjee, Amartya, Fitzsimmons, Maxwell, Song, Christopher, Liu, Jun
Solving nonlinear optimal control problems is a challenging task, particularly for high-dimensional problems. We propose algorithms for model-based policy iterations to solve nonlinear optimal control problems with convergence guarantees. The main co
Externí odkaz:
http://arxiv.org/abs/2402.10119
Diffusion models have emerged as a promising class of generative models that map noisy inputs to realistic images. More recently, they have been employed to generate solutions to partial differential equations (PDEs). However, they still struggle wit
Externí odkaz:
http://arxiv.org/abs/2402.08563
This paper introduces harmonic control Lyapunov barrier functions (harmonic CLBF) that aid in constrained control problems such as reach-avoid problems. Harmonic CLBFs exploit the maximum principle that harmonic functions satisfy to encode the proper
Externí odkaz:
http://arxiv.org/abs/2310.02869
Autor:
Mukherjee, Amartya, Liu, Jun
This paper proposes an actor-critic algorithm for controlling the temperature of a battery pack using a cooling fluid. This is modeled by a coupled 1D partial differential equation (PDE) with a controlled advection term that determines the speed of t
Externí odkaz:
http://arxiv.org/abs/2305.10952
Autor:
Mukherjee, Amartya, Liu, Jun
This paper introduces the Hamilton-Jacobi-Bellman Proximal Policy Optimization (HJBPPO) algorithm into reinforcement learning. The Hamilton-Jacobi-Bellman (HJB) equation is used in control theory to evaluate the optimality of the value function. Our
Externí odkaz:
http://arxiv.org/abs/2302.00237
Autor:
Anifowoshe, Abass Toba1,2, Mukherjee, Amartya1, Ajisafe, Victor A.3, Raichur, Ashok M.3, Nongthomba, Upendra1 upendra@iisc.ac.in
Publikováno v:
Scientific Reports. 9/3/2024, Vol. 14 Issue 1, p1-12. 12p.
Stochastic Parameterization using Compressed Sensing: Application to the Lorenz-96 Atmospheric Model
Growing set of optimization and regression techniques, based upon sparse representations of signals, to build models from data sets has received widespread attention recently with the advent of compressed sensing. This paper deals with the parameteri
Externí odkaz:
http://arxiv.org/abs/2106.14110