Search Results - "Physics-based Models"
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Authors: et al.
Source: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)Subject Terms: NMC lithium-ion battery, Equivalent circuit model, Physics-based models, Data granularity, Àrees temàtiques de la UPC::Enginyeria electrònica
File Description: application/pdf
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Authors: et al.
Source: Journal of Industrial Engineering and Management, Vol 18, Iss 3, Pp 427-458 (2025)
Subject Terms: predictive maintenance, data-driven models, physics-based models, knowledge-based models, industry 4.0., Industrial engineering. Management engineering, T55.4-60.8, Social Sciences, Commerce, HF1-6182, Business, HF5001-6182
File Description: electronic resource
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3
Authors:
Source: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)Subject Terms: Lithium-Ion, Physics Based Models, Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Enginyeria ambiental, Ageing Mechanisms, Battery Management Systems, 7. Clean energy
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Authors: et al.
Contributors: et al.
Source: 2023 IEEE Vehicle Power and Propulsion Conference (VPPC). :1-7
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Authors: et al.
Subject Terms: Medical Biochemistry and Metabolomics, Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Networking and Information Technology R&D (NITRD), Bioengineering, 1.1 Normal biological development and functioning, 1.4 Methodologies and measurements, Generic health relevance, cryo-EM, machine learning, ICA, AI for science, disentanglement, physics-based models, Biochemistry and cell biology, Medical biochemistry and metabolomics
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Authors: et al.
Source: Frontiers in Water, Vol 7 (2025)
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Authors:
Source: Results in Engineering, Vol 27, Iss , Pp 105975- (2025)
Subject Terms: Data-driven models, LSTM, Physics-based models, Streamflow, SWAT, WEAP, Technology
File Description: electronic resource
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8
Source: Advanced Materials. 36(45):1-39
Subject Terms: machine learning, nanocarriers, molecular modeling, physics-based models, data-driven models
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9
Authors: et al.
Contributors: et al.
Subject Terms: Àrees temàtiques de la UPC::Informàtica::Automàtica i control, Data granularity, Li-ion battery, Physics-based models, Equivalent circuit models, Data driven model, Digital twin
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10
Authors:
Subject Terms: 4007 Control Engineering, Mechatronics and Robotics, 40 Engineering, 7 Affordable and Clean Energy, 4009 Electronics, sensors and digital hardware, remaining useful life, hybrid prognostics, predictive maintenance, aircraft systems, data-driven models, physics-based models, hyper tangent boosted neural network (HTBNN)
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Relation: Fu S, Avdelidis NP, Plastropoulos A. (2025) Novel hybrid prognostics of aircraft systems. Electronics, Volume 14, Issue 11, May 2025, Article number 2193; https://doi.org/10.3390/electronics14112193; https://dspace.lib.cranfield.ac.uk/handle/1826/24047; 673593; 2193; 14; 11
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Model-Based Mechanical Property and Structural Failure Prediction of Pseudo Ductile Hybrid Composite
Authors:
Source: Journal of Building Material Science; Vol. 7 , Iss. 2 (June 2025): In Progress; 1-21 ; 2630-5216
Subject Terms: Failure Prediction, Mechanical Property Prediction, Pseudo Ductile Hybrid Composite, Data-Driven Models, Physics-Based Models, Hybrid Models, Electric Aircraft
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12
Authors: et al.
Contributors: et al.
Source: Advances in Physics: X, Vol 9, Iss 1 (2024)
Subject Terms: polymer materials modelling, Physics, QC1-999, 0103 physical sciences, Biopolymer modelling, physics-based models, 01 natural sciences, 0104 chemical sciences
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13
Authors:
Contributors:
Source: Lecture Notes in Networks and Systems ISBN: 9783031477171
Subject Terms: pedestrian predictions, deep learning pedestrian predictions crowds physics-based models, deep learning, [INFO] Computer Science [cs], physics-based models, crowds
File Description: application/pdf
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14
Authors: Maria Lupu
Source: Electronics, Communications and Computing (Editia 12)
Subject Terms: dynamic processes, deep neural networks, physics-based models, superconducting neural networks
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15
Authors: et al.
Contributors: et al.
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16
Authors: et al.
Subject Terms: Battery Management Systems, Equivalent Circuit Models, Battery, Physics-Based Models, Machine Learning, Energy storage, Lithium-ion battery, modeling, Aging mechanisms, Electrochemical model, Parameter identification
Relation: https://zenodo.org/communities/echarge4drivers/; https://zenodo.org/communities/eu/; https://zenodo.org/records/14443831; oai:zenodo.org:14443831; https://doi.org/10.1016/j.est.2024.113257
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17
Authors: et al.
Subject Terms: lithium-ion batteries, sustainability, physics-based models
Relation: https://zenodo.org/communities/irec/; https://zenodo.org/records/14224643; oai:zenodo.org:14224643; https://doi.org/10.3390/electronics13050860
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18
Authors: et al.
Contributors: et al.
Subject Terms: physics-based models, ICA, machine learning, cryo-EM, AI for science, disentanglement, Computer and information sciences, Biochemistry, cell and molecular biology
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Relation: The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the U.S. Department of Energy, under DOE Contract No. DE-AC02-76SF00515. DK was supported by the SLAC LDRD project \u201CAtomicSPI: Learning atomic scale biomolecular dynamics from single-particle imaging data\u201D (PI: FP). AH was supported by a CIFAR Fellowship and the Academy of Finland. NM acknowledges funding from grant NIH 1R01GM144965-01.; http://hdl.handle.net/10138/584582; 85199301990; 001274143500001
Availability: http://hdl.handle.net/10138/584582
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19
Authors:
Source: Pure and Applied Geophysics. 178:359-378
Subject Terms: Induced seismicity, Physics based models, Horn River Basin--Canada, Time-dependent Gutenberg-Richter parameters, Forecasting seismicity rates, 13. Climate action, Monte-Carlo simulations, 01 natural sciences, 6. Clean water, 0105 earth and related environmental sciences
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Authors: et al.
Contributors: et al.
Source: American Journal of Obstetrics and Gynecology. 224:16-34
Subject Terms: computation, Computational Biology, modeling, theory-based models, Models, Theoretical, physics-based models, 3. Good health, Obstetrics, 03 medical and health sciences, machine learning, 0302 clinical medicine, data, Gynecology, Humans, uncertainty, data-driven models
File Description: application/pdf
Access URL: https://www.repository.cam.ac.uk/bitstream/1810/309900/1/Computational%20Medicine%20the%20Present%20and%20the%20Future%209%20-%20final%20accepted.pdf
https://pubmed.ncbi.nlm.nih.gov/32841628
https://www.ncbi.nlm.nih.gov/pubmed/32841628
https://www.sciencedirect.com/science/article/pii/S0002937820308851
https://pubmed.ncbi.nlm.nih.gov/32841628/
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