Results 11 to 20 of about 11,198,121 (288)

Physics-Informed Machine Learning of Optical Modes in Composites

Conference on Lasers and Electro-Optics, 2022
We present machine learning techniques that incorporate physics into the training process. We demonstrate, on example of predicting light propagation in multilayered composites, that physics-informed models are significantly more robust than their black box counterparts.
Ghosh, Abantika, Elhamod, Mohannad, Bu, Jie, Lee, Wei-Cheng, Karpatne, Anuj, Podolskiy, Viktor A   +5 more
openaire   +3 more sources

Inverting the Kohn–Sham equations with physics-informed machine learning

Machine Learning: Science and Technology
Abstract Electronic structure theory calculations offer an understanding of matter at the quantum level, complementing experimental studies in materials science and chemistry. One of the most widely used methods, density functional theory, maps a set of real interacting electrons to a set of fictitious non-interacting electrons that ...
Vincent Martinetto, Karan Shah, Attila Cangi, Aurora Pribram-Jones   +3 more
openaire   +6 more sources

The Fusion Mechanism and Prospective Application of Physics-Informed Machine Learning in Bridge Lifecycle Health Monitoring

Infrastructures
Bridge health monitoring is crucial for ensuring the safety and durability of infrastructure. In traditional methods, physics-based models have high interpretability but are difficult to handle complex nonlinear problems, while purely data-driven machine
Jiaren Sun, Jiangjiang He, Guangbing Zhou, Jun Yang, Xiaoli Sun, Shuai Teng   +5 more
doaj   +2 more sources

MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning

CoRR, 2023
A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks. These OOD tasks require learning-to-learn from observations of the same (ODE) dynamical system with different unknown ODE parameters, and demand accurate forecasts even under out-of-support initial ...
S. Chandra Mouli, Muhammad Ashraful Alam, Bruno Ribeiro 0001   +2 more
openaire   +3 more sources

Physics-Informed Machine Learning for Smart Additive Manufacturing [PDF]

Procedia CIRP
Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data-driven ML's "black box" nature has presented a challenge to interpreting its outcomes.
Rahul Sharma, Maziar Raissi, Y. B. Guo
core   +5 more sources

Physics-informed Machine Learning with Uncertainty Quantification.

Doctor of ...
Daw, Arka
openaire   +2 more sources

Physics-informed machine learning for grade prediction in froth flotation [PDF]

Minerals Engineering
In this paper, physics-informed neural network models are developed to predict the concentrate gold grade in froth flotation cells. Accurate prediction of concentrate grades is important for the automatic control and optimization of mineral processing. Both first-principles and data-driven machine learning methods have been used to model the flotation ...
Nasiri Abarbekouh Mahdi, Iqbal Sahel, Särkkä Simo   +2 more
openaire   +5 more sources

Physics-Informed Machine Learning in Biomedical Science and Engineering. [PDF]

Annu Rev Biomed Eng
Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and ...
Ahmadi N, Cao Q, Humphrey JD, Karniadakis GE.   +3 more
europepmc   +5 more sources

Physics informed machine learning for chemistry tabulation

Journal of Computational Science, 2023
Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evolve, the two sub-systems are typically (re)solved separately.
Amol Salunkhe, Dwyer Deighan, Paul E. DesJardin, Varun Chandola   +3 more
openaire   +3 more sources

Physics-Informed Machine Learning for Structural Health Monitoring [PDF]

, 2021
The use of machine learning in Structural Health Monitoring is becoming more common, as many of the inherent tasks (such as regression and classification) in developing condition-based assessment fall naturally into its remit. This chapter introduces the concept of physics-informed machine learning, where one adapts ML algorithms to account for the ...
Elizabeth J. Cross, Samuel J. Gibson, Matthew R. Jones, Daniel J. Pitchforth, Sikai Zhang, Timothy J. Rogers 0003   +5 more
openaire   +3 more sources