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Flowchart of the training, validation, and testing phases of the ANN day-ah...
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in Modeling and Simulation of Solar Photovoltaic (PV) System
> Toward Better Photovoltaic SystemsDesign, Simulation, Optimization, Analysis, and Operations
Published: March 2023
FIG. 1.7 Flowchart of the training, validation, and testing phases of the ANN day-ahead PV power forecasting model ( Theocharides et al., 2020 ). More about this image found in Flowchart of the training, validation, and testing phases of the ANN day-ah...
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The procedure of training, validation, and testing of the DCNN model.
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in Monitoring, Fault Detection and Operations of Photovoltaic Systems
> Toward Better Photovoltaic SystemsDesign, Simulation, Optimization, Analysis, and Operations
Published: March 2023
FIG. 6.11 The procedure of training, validation, and testing of the DCNN model. More about this image found in The procedure of training, validation, and testing of the DCNN model.
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(a) Training loss (cost function) and accuracy variation, (b) confusion mat...
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in Monitoring, Fault Detection and Operations of Photovoltaic Systems
> Toward Better Photovoltaic SystemsDesign, Simulation, Optimization, Analysis, and Operations
Published: March 2023
FIG. 6.13 (a) Training loss (cost function) and accuracy variation, (b) confusion matrix. More about this image found in (a) Training loss (cost function) and accuracy variation, (b) confusion mat...
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Trajectories of the building space during the training period, where the te...
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in Control-Oriented Hybrid Modeling Framework for Building Thermal Modeling
> Energy Systems and ProcessesRecent Advances in Design and Control
Published: March 2023
FIG. 9.3 Trajectories of the building space during the training period, where the temperature setpoint trajectory is generated to excite the system dynamics (the setpoint trajectory is denoted by the dashed line). More about this image found in Trajectories of the building space during the training period, where the te...
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Block diagram for Levenberg–Marquardt based deep hybrid model training.
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in Modeling, Order-Reduction, and Controller Design of Hydraulic Fracturing
> Energy Systems and ProcessesRecent Advances in Design and Control
Published: March 2023
FIG. 12.10 Block diagram for Levenberg–Marquardt based deep hybrid model training. More about this image found in Block diagram for Levenberg–Marquardt based deep hybrid model training.
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Comparison of widths obtained from the hybrid model and the training data.
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in Modeling, Order-Reduction, and Controller Design of Hydraulic Fracturing
> Energy Systems and ProcessesRecent Advances in Design and Control
Published: March 2023
FIG. 12.11 Comparison of widths obtained from the hybrid model and the training data. More about this image found in Comparison of widths obtained from the hybrid model and the training data.
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One-day validation prediction of sensible cooling from the trained controll...
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in Control-Oriented Hybrid Modeling Framework for Building Thermal Modeling
> Energy Systems and ProcessesRecent Advances in Design and Control
Published: March 2023
FIG. 9.6 One-day validation prediction of sensible cooling from the trained controller model. More about this image found in One-day validation prediction of sensible cooling from the trained controll...
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The training and validation errors of the DP model: the square root of loss...
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in Learning DeePMD-Kit: A Guide to Building Deep Potential Models
> A Practical Guide to Recent Advances in Multiscale Modeling and Simulation of Biomolecules
Published: January 2023
FIG. 6.2 The training and validation errors of the DP model: the square root of loss for training (solid yellow) and validation (dashed yellow), root mean square errors (RMSEs) in force for training (solid blue) and validation (dashed blue), and RMSEs in energy for training (solid grey More about this image found in The training and validation errors of the DP model: the square root of loss...
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Results for DPRc model trained to ab initio DFT (PBE0/6-31...
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in Learning DeePMD-Kit: A Guide to Building Deep Potential Models
> A Practical Guide to Recent Advances in Multiscale Modeling and Simulation of Biomolecules
Published: January 2023
FIG. 6.4 Results for DPRc model trained to ab initio DFT (PBE0/6-31G*) QM/MM data ( Giese et al., 2022 ) for non-enzymatic reactions of a native system (all oxygen) and variants with thio substitutions at XP1 and X3′ positions. More about this image found in Results for DPRc model trained to ab initio DFT (PBE0/6-31...
Book Chapter
Control-Oriented Hybrid Modeling Framework for Building Thermal Modeling
Available to PurchaseSeries: AIPP Books, Methods
Published: March 2023
10.1063/9780735425743_009
EISBN: 978-0-7354-2574-3
ISBN: 978-0-7354-2572-9
... by a system of ordinary differential equations. These models can accurately capture the system dynamics and tend to provide reasonable extrapolation properties, i.e., accurately capture behavior beyond the operating range of the training dataset. However, developing a physics-based model requires a high...
Book Chapter
Series: AIPP Books, Professional
Published: March 2023
10.1063/9780735425712_016
EISBN: 978-0-7354-2571-2
ISBN: 978-0-7354-2568-2
..., consulting and support for faculty implementation for RBIS, and a supportive department showed 1/20 the number of quitters than the original study of Henderson et al. (2012) . The Science Teaching Fellows and Science Education Specialists were specially trained in pedagogy and educational research...
Book Chapter
Series: AIPP Books, Professional
Published: March 2023
10.1063/9780735425477_007
EISBN: 978-0-7354-2547-7
ISBN: 978-0-7354-2544-6
...” motions extend to the actual values of quantities that result from measurement. Consider, for example, a task developed by Panse et al. involving a man who walks from one end to the other of a train car that is itself moving relative to the track. Students were asked to compare how far the man...
Book Chapter
Physics Teacher Education for Early Science Learners
Available to PurchaseSeries: AIPP Books, Professional
Published: March 2023
10.1063/9780735425712_013
EISBN: 978-0-7354-2571-2
ISBN: 978-0-7354-2568-2
... results in significantly different pools of water in just a few minutes, which is consistent with the concentration span of very young learners. However, since teachers tend to teach as they have been taught, training preschool and primary teachers in physics and its methodology for teaching young...
Book Chapter
Data-Driven Adaptive Sparse Modeling of Chemical Process Systems
Available to PurchaseSeries: AIPP Books, Methods
Published: March 2023
10.1063/9780735425743_010
EISBN: 978-0-7354-2574-3
ISBN: 978-0-7354-2572-9
... dynamics in real-time. In the OASIS algorithm, multiple sets of historical process data from multiple operating conditions are collected as training data. Next, SINDy is applied to each dataset and multiple local SINDy models are identified. Further, a DNN was trained to learn the functional relationship...
Book Chapter
Modeling, Order-Reduction, and Controller Design of Hydraulic Fracturing
Available to PurchaseSeries: AIPP Books, Methods
Published: March 2023
10.1063/9780735425743_012
EISBN: 978-0-7354-2574-3
ISBN: 978-0-7354-2572-9
... process ( Sidhu et al., 2018 ; and Pahari et al., 2021 ). Additionally, the MOESP technique has been used to build a ROM that was used to train a Deep Reinforcement Learning controller in an offline manner, whose objective was to obtain a uniform concentration of proppant at the end...
Book Chapter
Monitoring, Fault Detection and Operations of Photovoltaic Systems
Available to PurchaseBook: Toward Better Photovoltaic Systems: Design, Simulation, Optimization, Analysis, and Operations
Series: AIPP Books, Principles
Published: March 2023
10.1063/9780735425613_006
EISBN: 978-0-7354-2561-3
ISBN: 978-0-7354-2560-6
... advantages and limits, and they vary in terms of accuracy, cost, complexity, the number of faults, required data, and real-time implementation. Methods based on ML and DL require a large amount of data for the training process. In the following example, a deep learning neural network (DCNN) is applied...
Book Chapter
Index
Available to PurchaseSeries: AIPP Books, Professional
Published: March 2023
10.1063/9780735425477_index
EISBN: 978-0-7354-2547-7
ISBN: 978-0-7354-2544-6
... research and extending to all the physical sciences including chemistry, mathematics, astronomy, and other related disciplines. Educational assessment (Education) Fundamental physics (General physics) Teacher training (Education) ...
Book Chapter
Index
Available to PurchaseSeries: AIPP Books, Professional
Published: March 2023
10.1063/9780735425514_index
EISBN: 978-0-7354-2551-4
ISBN: 978-0-7354-2548-4
..., and other related disciplines. Educational assessment (Education) Teacher training (Education) Fundamental physics (General physics) ...
Book
Energy Systems and Processes: Recent Advances in Design and Control
Available to PurchaseSeries: AIPP Books, Methods
Published: March 2023
10.1063/9780735425743
EISBN: 978-0-7354-2574-3
ISBN: 978-0-7354-2572-9
Book Chapter
References
Available to PurchaseSeries: AIPP Books, Professional
Published: March 2023
EISBN: 978-0-7354-2571-2
ISBN: 978-0-7354-2568-2
...References References Abbott , R. D. et al. , Teaching Assistant Training in the 1990s. Directions for Teaching and Learning, No. 39 , edited by J. D. Nyquist et al. ( Jossey-Bass , San Francisco , 1989 ). Aguilar , L. et al. , Phys. Today 67 ( 5 ), 43 ( 2014...
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