Zobrazeno 1 - 10
of 1 439
pro vyhledávání: '"Renganathan P"'
Autor:
Xu, Shaoming, Renganathan, Arvind, Khandelwal, Ankush, Ghosh, Rahul, Li, Xiang, Liu, Licheng, Tayal, Kshitij, Harrington, Peter, Jia, Xiaowei, Jin, Zhenong, Nieber, Jonh, Kumar, Vipin
Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning models have achieved state-of-the-art results of streamflow prediction,
Externí odkaz:
http://arxiv.org/abs/2410.14137
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. Traditional time series approaches for prediction often focus on either autoregressive modeling, which relies solely on past obse
Externí odkaz:
http://arxiv.org/abs/2410.12184
We tackle the problem of quantifying failure probabilities for expensive computer experiments with stochastic inputs. The computational cost of evaluating the computer simulation prohibits direct Monte Carlo (MC) and necessitates a statistical surrog
Externí odkaz:
http://arxiv.org/abs/2410.04496
This paper investigates the stereographic projection of points along the Nyquist plots of single input single output (SISO) linear time invariant (LTI) systems subject to probabilistic uncertainty. At each frequency, there corresponds a complex-value
Externí odkaz:
http://arxiv.org/abs/2409.17613
Hierarchically Disentangled Recurrent Network for Factorizing System Dynamics of Multi-scale Systems
Autor:
Ghosh, Rahul, McEachran, Zac, Renganathan, Arvind, Lindsay, Kelly, Sharma, Somya, Steinbach, Michael, Nieber, John, Duffy, Christopher, Kumar, Vipin
We present a knowledge-guided machine learning (KGML) framework for modeling multi-scale processes, and study its performance in the context of streamflow forecasting in hydrology. Specifically, we propose a novel hierarchical recurrent neural archit
Externí odkaz:
http://arxiv.org/abs/2407.20152
We extend the notion of Cantor-Kantorovich distance between Markov chains introduced by (Banse et al., 2023) in the context of Markov Decision Processes (MDPs). The proposed metric is well-defined and can be efficiently approximated given a finite ho
Externí odkaz:
http://arxiv.org/abs/2407.08324
Autor:
Renganathan, S. Ashwin, Carlson, Kade
Emulating the mapping between quantities of interest and their control parameters using surrogate models finds widespread application in engineering design, including in numerical optimization and uncertainty quantification. Gaussian process models c
Externí odkaz:
http://arxiv.org/abs/2407.01495
Control of network systems with uncertain local dynamics has remained an open problem for a long time. In this paper, a distributed minimax adaptive control algorithm is proposed for such networks whose local dynamics has an uncertain parameter possi
Externí odkaz:
http://arxiv.org/abs/2310.17364
Classical evolutionary approaches for multiobjective optimization are quite accurate but incur a lot of queries to the objectives; this can be prohibitive when objectives are expensive oracles. A sample-efficient approach to solving multiobjective op
Externí odkaz:
http://arxiv.org/abs/2310.15788
We introduce TAM-RL (Task Aware Modulation using Representation Learning), a novel multimodal meta-learning framework for few-shot learning in heterogeneous systems, designed for science and engineering problems where entities share a common underlyi
Externí odkaz:
http://arxiv.org/abs/2310.04727