Výsledky vyhledávání - Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification

Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework Autor Jin-Long, Wu, Xiao, Heng, Paterson, Eric

“… Recently, Wang et al. demonstrated that machine learning can be used to improve the RANS modeled Reynolds stresses by leveraging data from high fidelity simulations…”
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Recent progress in augmenting turbulence models with physics-informed machine learning Autor Zhang, Xinlei, Wu, Jinlong, Coutier-Delgosha, Olivier, Xiao, Heng

“… This paper presents some of the recent progresses in our group on augmenting turbulence models with physics-informed machine learning…”
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Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community Autor Zahura, Faria T., Goodall, Jonathan L., Sadler, Jeffrey M., Shen, Yawen, Morsy, Mohamed M., Behl, Madhur

“…Mitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective flood prediction for prompt action. Typically, physics‐based 1‐D pipe/2…”
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A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models Autor Roy, Abhinanda, Kasiviswanathan, K. S., Patidar, Sandhya, Adeloye, Adebayo J., Soundharajan, Bankaru‐Swamy, Ojha, Chandra Shekhar P.

“… arising from model inputs, parameters, and structure. Despite several attempts to quantify the model prediction uncertainty, reducing the same for improving the reliability of models is indispensable for their wider acceptance…”
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Dynamic energy system modeling using hybrid physics-based and machine learning encoder–decoder models Autor Machalek, Derek, Tuttle, Jake, Andersson, Klas, Powell, Kody M.

“… The evaluated models are a pure machine learning model, a novel hybrid machine learning and physics-based model, and the hybrid model with an incomplete dataset…”
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Physics‐Based Machine Learning Electroluminescence Models for Fast yet Accurate Solar Cell Characterization Autor Laot, Erell, Puel, Jean‐Baptiste, Guillemoles, Jean‐François, Ory, Daniel

“… A derived physical model enables the determination of two local pseudoparameters from ELV data measured on silicon solar cells…”
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Projection-based model reduction: Formulations for physics-based machine learning Autor Swischuk, Renee, Mainini, Laura, Peherstorfer, Benjamin, Willcox, Karen

“…•New approach for physics-based machine learning using POD expansions.•Machine learning methods learn map between inputs and POD coefficients…”
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AdjointNet: Constraining machine learning models with physics-based codes Autor Karra, Satish, Ahmmed, Bulbul, Mudunuru, Maruti K

“…Physics-informed Machine Learning has recently become attractive for learning physical parameters and features from simulation and observation data…”
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Sequence-based Machine Learning Models in Jet Physics Autor Teixeira de Lima, Rafael

“… In particular, Machine Learning algorithms with sequences as inputs have seen successfull applications to important problems, such as Natural Language Processing (NLP…”
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Hybrid Physics-Based and Machine Learning Models for Reservoir Simulations

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Interpretable machine learning models: a physics-based view Autor Matei, Ion, de Kleer, Johan, Somarakis, Christoforos, Rai, Rahul, Baras, John S

“…To understand changes in physical systems and facilitate decisions, explaining how model predictions are made is crucial…”
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Variability Aware FET Model With Physics Knowledge Based Machine Learning Autor Sheelvardhan, Kumar, Guglani, Surila, Ehteshamuddin, M., Roy, Sourajeet, Dasgupta, Avirup

“…) using various machine learning (ML) architectures. This paper provides a detailed comparison of the various architectures…”
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Physics-Based Propagation Models Enabled by Machine Learning Autor Seretis, Aristeidis

“… To that end, channel propagation models are indispensable. Such models can be used to optimize the position of wireless access points, assess interference from and towards…”
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Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials Autor Hsu, Tim, Epting, William K., Kim, Hokon, Abernathy, Harry W., Hackett, Gregory A., Rollett, Anthony D., Salvador, Paul A., Holm, Elizabeth A.

“…). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN-generated microstructures closely match the performance distribution…”
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Machine‐Learning (ML)‐Physics Fusion Model Accelerates the Paradigm Shift in Typhoon Forecasting With a CNOP‐Based Assimilation Framework Autor Niu, Zeyi, Wang, Dongliang, Mu, Mu, Huang, Wei, Fan, Xuliang, Yang, Mengqi, Qin, Bo

“…‐learning model with the physics‐based Shanghai Typhoon Model (SHTM). By employing spectral nudging, the hybrid FuXi…”
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RETRACTED: A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models Autor Roy, Abhinanda, Kasiviswanathan, K. S., Patidar, Sandhya, Adeloye, Adebayo J., Soundharajan, Bankaru‐Swamy, Ojha, Chandra Shekhar P.

“… arising from model inputs, parameters, and structure. Despite several attempts to quantify the model prediction uncertainty, reducing the same for improving the reliability of models is indispensable for their wider acceptance…”
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A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy Autor Roy, Abhinanda, Kasiviswanathan, K. S., Patidar, Sandhya, Adeloye, Adebayo J., Soundharajan, Bankaru‐Swamy, Ojha, Chandra Shekhar P.

“… This paper aims to investigate the potential of a novel hybrid modeling framework that couples the random forest algorithm, particle filter, and the HBV model for improving the overall accuracy…”
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Comparing Physics‐Based, Conceptual and Machine‐Learning Models to Predict Groundwater Levels by BMA Autor Wöhling, Thomas, Delgadillo, Alvaro Oliver Crespo, Kraft, Moritz, Guthke, Anneli

“…), an eigenmodel, a transfer‐function model, and three machine learning models, namely, multi‐layer perceptron models, long short…”
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A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics‐Based Component Autor Arcomano, Troy, Szunyogh, Istvan, Wikner, Alexander, Hunt, Brian R., Ott, Edward

“…It is shown that a recently developed hybrid modeling approach that combines machine learning (ML…”
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Prediction Beyond the Medium Range With an Atmosphere‐Ocean Model That Combines Physics‐Based Modeling and Machine Learning Autor Patel, Dhruvit, Arcomano, Troy, Hunt, Brian, Szunyogh, Istvan, Ott, Edward

“…This paper explores the potential of a hybrid modeling approach that combines machine learning (ML…”
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