Search Results for author:
Found 17 papers, 2 papers with code
Neural Differential Algebraic Equations
In this work, we show that the proposed NDAEs abstraction is suitable for relevant system-theoretic data-driven modeling tasks.
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
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.
Contextual Reinforcement Learning for Offshore Wind Farm Bidding
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.
Physics-Informed Polynomial Chaos Expansions
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.
Learning thermodynamically constrained equations of state with uncertainty
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.
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning
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.
How do Bounce Rates vary according to product sold?
Bounce Rate of different E-commerce websites depends on the different factors based upon the different devices through which traffic share is observed.
Developing and Validating Semi-Markov Occupancy Generative Models: A Technical Report
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).
Deploying deep learning in OpenFOAM with TensorFlow
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.
Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study
This analysis of training a BNN at scale outlines the limitations and benefits compared to a conventional neural network.
A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic
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.
Learning a Deep Convolution Network with Turing Test Adversaries for Microscopy Image Super Resolution
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.
Developing a Portable Natural Language Processing Based Phenotyping System
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.
Using lexical and Dependency Features to Disambiguate Discourse Connectives in Hindi
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.
