Knowledge Guided Machine Learning
portes grátis
Knowledge Guided Machine Learning
Accelerating Discovery using Scientific Knowledge and Data
Kannan, Ramakrishnan; Kumar, Vipin; Karpatne, Anuj
Taylor & Francis Ltd
08/2024
430
Mole
9780367698201
15 a 20 dias
Descrição não disponível.
About the Editors. List of Contributors. 1 Introduction. 2 Targeted Use of Deep Learning for Physics and Engineering. 3 Combining Theory and Data-Driven Approaches for Epidemic Forecasts. 4 Machine Learning and Projection-Based Model Reduction in Hydrology and Geosciences. 5 Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey. 6 Adaptive Training Strategies for Physics-Informed Neural Networks. 7 Modern Deep Learning for Modeling Physical Systems. 8 Physics-Guided Deep Learning for Spatiotemporal Forecasting. 9 Science-Guided Design and Evaluation of Machine Learning Models: A Case-Study on Multi-Phase Flows. 10 Using the Physics of Electron Beam Interactions to Determine Optimal Sampling and Image Reconstruction Strategies for High Resolution STEM. 11 FUNNL: Fast Nonlinear Nonnegative Unmixing for Alternate Energy Systems. 12 Structure Prediction from Scattering Profiles: A Neutron-Scattering Use-Case. 13 Physics-Infused Learning: A DNN and GAN Approach. 14 Combining System Modeling and Machine Learning into Hybrid Ecosystem Modeling. 15 Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature Modeling. 16 Physics-Guided Recurrent Neural Networks for Predicting Lake Water Temperature. 17 Physics-Guided Architecture (PGA) of LSTM Models for Uncertainty Quantification in Lake Temperature Modeling, Index.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
deep learning;machine learning;neural networks;physics;system modeling;Physics-Guided;Ml Model;Deep Learning Models;Test RMSE;Lake Mendota;Deep Neural Networks;DNN Model;DMD;ROMs;MSE Loss;Physical Inconsistency;Ml Method;Physically Consistent;Loss Function;Energy Conservation;ABM;Data Science Models;Pod Mode;Beam Influence;MPC;Abundance Maps;Net Ecosystem Exchange;Ml Framework;Training Fraction;Indian Pines Dataset;Reduced Order System
About the Editors. List of Contributors. 1 Introduction. 2 Targeted Use of Deep Learning for Physics and Engineering. 3 Combining Theory and Data-Driven Approaches for Epidemic Forecasts. 4 Machine Learning and Projection-Based Model Reduction in Hydrology and Geosciences. 5 Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey. 6 Adaptive Training Strategies for Physics-Informed Neural Networks. 7 Modern Deep Learning for Modeling Physical Systems. 8 Physics-Guided Deep Learning for Spatiotemporal Forecasting. 9 Science-Guided Design and Evaluation of Machine Learning Models: A Case-Study on Multi-Phase Flows. 10 Using the Physics of Electron Beam Interactions to Determine Optimal Sampling and Image Reconstruction Strategies for High Resolution STEM. 11 FUNNL: Fast Nonlinear Nonnegative Unmixing for Alternate Energy Systems. 12 Structure Prediction from Scattering Profiles: A Neutron-Scattering Use-Case. 13 Physics-Infused Learning: A DNN and GAN Approach. 14 Combining System Modeling and Machine Learning into Hybrid Ecosystem Modeling. 15 Physics-Guided Neural Networks (PGNN): An Application in Lake Temperature Modeling. 16 Physics-Guided Recurrent Neural Networks for Predicting Lake Water Temperature. 17 Physics-Guided Architecture (PGA) of LSTM Models for Uncertainty Quantification in Lake Temperature Modeling, Index.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
deep learning;machine learning;neural networks;physics;system modeling;Physics-Guided;Ml Model;Deep Learning Models;Test RMSE;Lake Mendota;Deep Neural Networks;DNN Model;DMD;ROMs;MSE Loss;Physical Inconsistency;Ml Method;Physically Consistent;Loss Function;Energy Conservation;ABM;Data Science Models;Pod Mode;Beam Influence;MPC;Abundance Maps;Net Ecosystem Exchange;Ml Framework;Training Fraction;Indian Pines Dataset;Reduced Order System
