Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
SA Faroughi, NM Pawar… - Journal of …, 2024 - asmedigitalcollection.asme.org
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 …
[HTML][HTML] A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation
Accurate fatigue assessment of material plagued by defects is of utmost importance to
guarantee safety and service continuity in engineering components. This study shows how …
guarantee safety and service continuity in engineering components. This study shows how …
Physics-Informed Fully Convolutional Networks for Forward Prediction of Temperature Field and Inverse Estimation of Thermal Diffusivity
Physics-informed neural networks (PINNs) are a novel approach to solving partial
differential equations (PDEs) through deep learning. They offer a unified manner for solving …
differential equations (PDEs) through deep learning. They offer a unified manner for solving …
Early Prediction of Human Intention for Human–Robot Collaboration Using Transformer Network
Human intention prediction plays a critical role in human–robot collaboration, as it helps
robots improve efficiency and safety by accurately anticipating human intentions and …
robots improve efficiency and safety by accurately anticipating human intentions and …
Machine learning-driven high-fidelity ensemble surrogate modeling of Francis turbine unit based on data-model interactive simulation
Abnormal mechanical properties of Francis turbine units (FTUs) lead to unstable output
power and operation fault, and may cause catastrophic hazards. At present, computational …
power and operation fault, and may cause catastrophic hazards. At present, computational …
Physics-constrained neural networks with minimax architecture for multiphysics dendritic growth problems in additive manufacturing
D Liu, Y Wang - Manufacturing Letters, 2023 - Elsevier
Data sparsity is the main barrier to apply deep neural networks to solve complex scientific
and engineering problems, where it is expensive to obtain a large amount of high-fidelity …
and engineering problems, where it is expensive to obtain a large amount of high-fidelity …
Deep Learning in Computational Design Synthesis: A Comprehensive Review
S Kumar Singh, R Rai… - Journal of …, 2024 - asmedigitalcollection.asme.org
A paradigm shift in the computational design synthesis (CDS) domain is being witnessed by
the onset of the innovative usage of machine learning techniques. The rapidly evolving …
the onset of the innovative usage of machine learning techniques. The rapidly evolving …
Comparative Analysis of Convolutional Neural Network Architectures for Automated Knee Segmentation in Medical Imaging: A Performance Evaluation
Segmentation of anatomical components is a major step in creating accurate and realistic
3D models of the human body, which are used in many clinical applications, including …
3D models of the human body, which are used in many clinical applications, including …
Fairness-and Uncertainty-Aware Data Generation for Data-Driven Design Based on Active Learning
The design dataset is the backbone of data-driven design. Ideally, the dataset should be
fairly distributed in both shape and property spaces to efficiently explore the underlying …
fairly distributed in both shape and property spaces to efficiently explore the underlying …