Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Nair, Siddharth"'
In this paper, we introduce a physics and geometry informed neural operator network with application to the forward simulation of acoustic scattering. The development of geometry informed deep learning models capable of learning a solution operator f
Externí odkaz:
http://arxiv.org/abs/2406.03407
Multi-Objective Learning Model Predictive Control is a novel data-driven control scheme which improves a linear system's closed-loop performance with respect to several convex control objectives over iterations of a repeated task. At each task iterat
Externí odkaz:
http://arxiv.org/abs/2405.11698
This paper extends the finite element network analysis (FENA) to include a dynamic time-transient formulation. FENA was initially formulated in the context of the linear static analysis of 1D and 2D elastic structures. By introducing the concept of s
Externí odkaz:
http://arxiv.org/abs/2407.02494
Publikováno v:
Engineering with Computers (2024)
This work presents a physics-driven machine learning framework for the simulation of acoustic scattering problems. The proposed framework relies on a physics-informed neural network (PINN) architecture that leverages prior knowledge based on the phys
Externí odkaz:
http://arxiv.org/abs/2403.04094
We propose a hierarchical architecture designed for scalable real-time Model Predictive Control (MPC) in complex, multi-modal traffic scenarios. This architecture comprises two key components: 1) RAID-Net, a novel attention-based Recurrent Neural Net
Externí odkaz:
http://arxiv.org/abs/2402.01116
We propose a Stochastic MPC (SMPC) formulation for path planning with autonomous vehicles in scenarios involving multiple agents with multi-modal predictions. The multi-modal predictions capture the uncertainty of urban driving in distinct modes/mane
Externí odkaz:
http://arxiv.org/abs/2310.20561
We propose a supervised learning framework for computing solutions of multi-parametric Mixed Integer Linear Programs (MILPs) that arise in Model Predictive Control. Our approach also quantifies sub-optimality for the computed solutions. Inspired by B
Externí odkaz:
http://arxiv.org/abs/2303.12152
We propose an iterative approach for designing Robust Learning Model Predictive Control (LMPC) policies for a class of nonlinear systems with additive, unmodelled dynamics. The nominal dynamics are assumed to be difference flat, i.e., the state and i
Externí odkaz:
http://arxiv.org/abs/2303.12127
Publikováno v:
Comput. Methods Appl. Mech. Engrg. 414(2023)116167
This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many pra
Externí odkaz:
http://arxiv.org/abs/2302.07504
Motion planning for autonomous vehicles sharing the road with human drivers remains challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi-modal, 2) interacting with the autonomous vehicle, and 3) actively makin
Externí odkaz:
http://arxiv.org/abs/2302.00060