Zobrazeno 1 - 10
of 3 908
pro vyhledávání: '"Maulik, P."'
Predicting the long-term behavior of chaotic systems remains a formidable challenge due to their extreme sensitivity to initial conditions and the inherent limitations of traditional data-driven modeling approaches. This paper introduces a novel fram
Externí odkaz:
http://arxiv.org/abs/2410.05572
Modern-world robotics involves complex environments where multiple autonomous agents must interact with each other and other humans. This necessitates advanced interactive multi-agent motion planning techniques. Generalized Nash equilibrium(GNE), a s
Externí odkaz:
http://arxiv.org/abs/2410.05554
When working around humans, it is important to model their perception limitations in order to predict their behavior more accurately. In this work, we consider agents with a limited field of view, viewing range, and ability to miss objects within vie
Externí odkaz:
http://arxiv.org/abs/2410.05547
Autor:
Barwey, Shivam, Balin, Riccardo, Lusch, Bethany, Patel, Saumil, Balakrishnan, Ramesh, Pal, Pinaki, Maulik, Romit, Vishwanath, Venkatram
This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical cons
Externí odkaz:
http://arxiv.org/abs/2410.01657
Autor:
Malik, Zachariah, Maulik, Romit
Ensemble Kalman Filtering (EnKF) is a popular technique for data assimilation, with far ranging applications. However, the vanilla EnKF framework is not well-defined when perturbations are nonlinear. We study two non-linear extensions of the vanilla
Externí odkaz:
http://arxiv.org/abs/2409.14300
Dynamic games offer a versatile framework for modeling the evolving interactions of strategic agents, whose steady-state behavior can be captured by the Nash equilibria of the games. Nash equilibria are often computed in feedback, with policies depen
Externí odkaz:
http://arxiv.org/abs/2409.11257
The celebrated Takens' embedding theorem provides a theoretical foundation for reconstructing the full state of a dynamical system from partial observations. However, the classical theorem assumes that the underlying system is deterministic and that
Externí odkaz:
http://arxiv.org/abs/2409.08768
Autor:
Barwey, Shivam, Pal, Pinaki, Patel, Saumil, Balin, Riccardo, Lusch, Bethany, Vishwanath, Venkatram, Maulik, Romit, Balakrishnan, Ramesh
A graph neural network (GNN) approach is introduced in this work which enables mesh-based three-dimensional super-resolution of fluid flows. In this framework, the GNN is designed to operate not on the full mesh-based field at once, but on localized
Externí odkaz:
http://arxiv.org/abs/2409.07769
We show that birational hyper-K\"ahler varieties of $K3^{[n]}$-type are derived equivalent, establishing the D-equivalence conjecture in these cases. The Fourier-Mukai kernels of our derived equivalences are constructed from projectively hyperholomor
Externí odkaz:
http://arxiv.org/abs/2408.14775
Autor:
Jain, Vinamr, Maulik, Romit
Forecasting dynamical systems is of importance to numerous real-world applications. When possible, dynamical systems forecasts are constructed based on first-principles-based models such as through the use of differential equations. When these equati
Externí odkaz:
http://arxiv.org/abs/2407.21602