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Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
Solution approaches to inverse heat transfer problems with and without phase changes: A state-of-the-art review
Heat transfer problems (HTPs) with and without phase change are encountered in many
areas of science and engineering. Some HTPs cannot be solved straightforwardly since …
areas of science and engineering. Some HTPs cannot be solved straightforwardly since …
Practical uncertainty quantification for space-dependent inverse heat conduction problem via ensemble physics-informed neural networks
Inverse heat conduction problems (IHCPs) are problems of estimating unknown quantities of
interest (QoIs) of the heat conduction with given temperature observations. The challenge of …
interest (QoIs) of the heat conduction with given temperature observations. The challenge of …
Learning in PINNs: Phase transition, total diffusion, and generalization
SJ Anagnostopoulos, JD Toscano… - ar** accurate dynamic models for various systems is crucial for optimization, control,
fault diagnosis, and prognosis. Recent advancements in information technologies and …
fault diagnosis, and prognosis. Recent advancements in information technologies and …
[HTML][HTML] A self-supervised learning framework based on physics-informed and convolutional neural networks to identify local anisotropic permeability tensor from …
In liquid composite molding processes, variabilities in material and process conditions can
lead to distorted flow patterns during filling. These distortions appear not only within the …
lead to distorted flow patterns during filling. These distortions appear not only within the …
Breast cancer detection using enhanced IRI-numerical engine and inverse heat transfer modeling: model description and clinical validation
Effective treatment of breast cancer relies heavily on early detection. Routine annual
mammography is a widely accepted screening technique that has resulted in significantly …
mammography is a widely accepted screening technique that has resulted in significantly …
Multifidelity physics-constrained neural networks with minimax architecture
Data sparsity is still the main challenge to apply machine learning models to solve complex
scientific and engineering problems. The root cause is the “curse of dimensionality” in …
scientific and engineering problems. The root cause is the “curse of dimensionality” in …