Physics-informed machine learning for composition – process – property design: Shape memory alloy demonstration

TL;DR: In this paper, a machine learning-based alloy design system (MADS) was presented to facilitate the rational design of high entropy alloys with enhanced hardness, and the best performer exhibits ultra-high hardness, which is 24.8% higher than the highest one.

TL;DR: A review of the history of physics-based ML and its application in civil engineering can be found in this article, where the authors reviewed the history and application of physics based ML models in the field of civil engineering.

TL;DR: In this article, a deep neural network and a physics-informed neural network were applied to predict the creep-fatigue life of 316 stainless steel, and the results showed that the physics-informed neural network presented better prediction accuracy than deep neural networks and conventional machine learning models.

TL;DR: In this paper , a PINN approach was proposed to solve the equations of coupled flow and deformation in porous media for both single-phase and multiphase flow, and the solution space was constructed using multi-layer neural networks.

TL;DR: CRC handbook of chemistry and physics, CRC Handbook of Chemistry and Physics, CRC handbook as discussed by the authors, CRC Handbook for Chemistry and Physiology, CRC Handbook for Physics,

TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?

TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.