no code implementations • 19 Mar 2024 • James Koch, Madelyn Shapiro, Himanshu Sharma, Draguna Vrabie, Jan Drgona
In this work, we show that the proposed NDAEs abstraction is suitable for relevant system-theoretic data-driven modeling tasks.
no code implementations • 23 Feb 2024 • Himanshu Sharma, Lukáš Novák, Michael D. Shields
We present a novel physics-constrained polynomial chaos expansion as a surrogate modeling method capable of performing both scientific machine learning (SciML) and uncertainty quantification (UQ) tasks.
no code implementations • 18 Dec 2023 • David Cole, Himanshu Sharma, Wei Wang
We propose a framework for applying reinforcement learning to contextual two-stage stochastic optimization and apply this framework to the problem of energy market bidding of an off-shore wind farm.
no code implementations • 4 Sep 2023 • Lukáš Novák, Himanshu Sharma, Michael D. Shields
This paper presents a novel methodology for the construction of physics-informed polynomial chaos expansions (PCE) that combines the conventional experimental design with additional constraints from the physics of the model.
no code implementations • 29 Jun 2023 • Himanshu Sharma, Jim A. Gaffney, Dimitrios Tsapetis, Michael D. Shields
Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in the EOS predictions.
no code implementations • 20 Dec 2022 • Aowabin Rahman, Arnab Bhattacharya, Thiagarajan Ramachandran, Sayak Mukherjee, Himanshu Sharma, Ted Fujimoto, Samrat Chatterjee
Search and Rescue (SAR) missions in remote environments often employ autonomous multi-robot systems that learn, plan, and execute a combination of local single-robot control actions, group primitives, and global mission-oriented coordination and collaboration.
no code implementations • 13 May 2022 • Himanshu Sharma
Bounce Rate of different E-commerce websites depends on the different factors based upon the different devices through which traffic share is observed.
no code implementations • 21 Dec 2021 • Soumya Kundu, Saptarshi Bhattacharya, Himanshu Sharma, Veronica Adetola
This report documents recent technical work on developing and validating stochastic occupancy models in commercial buildings, performed by the Pacific Northwest National Laboratory (PNNL) as part of the Sensor Impact Evaluation and Verification project under the U. S. Department of Energy (DOE) Building Technologies Office (BTO).
2 code implementations • 1 Dec 2020 • Romit Maulik, Himanshu Sharma, Saumil Patel, Bethany Lusch, Elise Jennings
We outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive tasks.
no code implementations • 23 May 2020 • Himanshu Sharma, Elise Jennings
This analysis of training a BNN at scale outlines the limitations and benefits compared to a conventional neural network.
no code implementations • 16 Mar 2020 • Rohan Pandey, Vaibhav Gautam, Ridam Pal, Harsh Bandhey, Lovedeep Singh Dhingra, Himanshu Sharma, Chirag Jain, Kanav Bhagat, Arushi, Lajjaben Patel, Mudit Agarwal, Samprati Agrawal, Rishabh Jalan, Akshat Wadhwa, Ayush Garg, Vihaan Misra, Yashwin Agrawal, Bhavika Rana, Ponnurangam Kumaraguru, Tavpritesh Sethi
Conclusion: We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation.
no code implementations • 18 Jan 2019 • Francis Tom, Himanshu Sharma, Dheeraj Mundhra, Tathagato Rai Dastidar, Debdoot Sheet
Adversarially trained deep neural networks have significantly improved performance of single image super resolution, by hallucinating photorealistic local textures, thereby greatly reducing the perception difference between a real high resolution image and its super resolved (SR) counterpart.
1 code implementation • 17 Jul 2018 • Himanshu Sharma, Chengsheng Mao, Yizhen Zhang, Haleh Vatani, Liang Yao, Yizhen Zhong, Luke Rasmussen, Guoqian Jiang, Jyotishman Pathak, Yuan Luo
Our system facilitates portable phenotyping of obesity and its 15 comorbidities based on the unstructured patient discharge summaries, while achieving a performance that often ranked among the top 10 of the challenge participants.
no code implementations • LREC 2016 • Rohit Jain, Himanshu Sharma, Dipti Sharma
We report that the novel dependency features introduced have a higher impact on precision, in comparison to the syntactic features previously used for this task.