SEARCH · Engineering Papers
Results for “physical learning”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Predicting fusion ignition at the National Ignition Facility with physics-informed deep learning
Here, an inertial confinement fusion experiment, carried out at the National Ignition Facility, has achieved ignition by generating fusion energy exceeding the laser energy that drove the experiment. Prior to the experiment, a generative machine learning model that combines radiation hydrodynamics simulations, deep learning, experimental data, and Bayesian statistics was used to predict, with a probability greater than 70%, that ignition was the most likely outcome for this shot.
Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling
Not provided.
SmartTensors: Unsupervised and Physics-informed Machine Learning Framework for the Geoscience Applications
Explore the source record for details and available documents.
Physics-Coupled Machine Learning for Geological Carbon Storage Evaluation and Performance Analysis
16th International Conference on Greenhouse Gas Technologies (GHGT-16), Lyon, France, October 23-27, 2022
Physics-Informed Deep Learning for Multiscale Water Cycle Prediction
This paper falls under Focal Area #2: Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI-driven model/component/parameterization selection).
Assembling training datsets for generalizable machine-learned models of physical phenomena.
Abstract not provided.
Data-driven learning of nonlocal physics from high-fidelity synthetic data.
Abstract not provided.
Physics-informed machine learning of flow and reactive transport in porous media.
Abstract not provided.
Data-driven learning of nonlocal physics from high-fidelity synthetic data.
Abstract not provided.
Machine learning of Physics-InformedGraph Neural Networks from TCAD models.
Abstract not provided.
Physics-Informed Machine Learning Surrogates with Optimization-Based Guarantees: Applications to AC Power Flow.
Abstract not provided.
Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)
This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.
Physics-based Deep Learning Driven CO2 Flow Modeling and Data Assimilation for Real-Time Forecasting.
Abstract not provided.
Physics-based Deep Learning Driven CO2 Flow Modeling and Data Assimilation for Real-Time Forecasting.
Abstract not provided.
Physics-informed Machine Learning for Development of Interpretable, Improved Material Damage Models.
Abstract not provided.
Probabilistic data fusion and physics-informed machine learning: A new paradigm for modeling under uncertainty, and its application to accelerating the discovery of new materials
In this report we summarize the work conducted by PI Perdikaris and his group under this Early Career project DE–SC0019116 during the period of 09/01/2018 – 08/31/2023. The central aim of the work was to introduce a new paradigm for scientific data analysis that can seamlessly synthesize rigorous mathematical modeling with data of variable fidelity (e.g., measurements at multiple scales/resolutions or predictions of variable fidelity models) and multiple modalities (e.g., images, time–series, or scattered measurements). The setting we are interested in involves complex systems that are partially observed and whose dynamical behavior could be hard to model or totally unknown. The inherent uncertainty associated with this setting necessitates a departure from the classical deterministic realm of modeling and scientific computation, and, consequently, our main building blocks can no longer be crisp deterministic numbers and governing laws, but instead we must operate with probabilistic models.