Results 41 to 50 of about 11,198,121 (288)

Physics-informed machine learning for inorganic scintillator discovery

The Journal of Chemical Physics, 2018
Applications of inorganic scintillators—activated with lanthanide dopants, such as Ce and Eu—are found in diverse fields. As a strict requirement to exhibit scintillation, the 4f ground state (with the electronic configuration of [Xe]4fn 5d0) and 5d1 lowest excited state (with the electronic configuration of [Xe]4fn−1 5d1) levels induced by the ...
G. Pilania, K. J. McClellan, C. R. Stanek, B. P. Uberuaga   +3 more
openaire   +3 more sources

Physics-Informed Machine Learning Improves Detection of Head Impacts

Annals of Biomedical Engineering, 2022
In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Monitoring player impacts is vitally important to understanding and protecting from injuries like concussion.
Samuel J. Raymond, Nicholas J. Cecchi, Hossein Vahid Alizadeh, Ashlyn A. Callan, Eli Rice, Yuzhe Liu, Zhou Zhou, Michael Zeineh, David B. Camarillo   +8 more
openaire   +3 more sources

Reconstructing enzyme evolution by protein engineering

FEBS Letters, EarlyView.
Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler, Markus R. Busch, Reinhard Sterner   +2 more
wiley   +1 more source

Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

Advanced Photonics Research, 2023
Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices.
Sulagna Sarkar, Anqi Ji, Zachary Jermain, Robert Lipton, Mark Brongersma, Kaushik Dayal, Hae Young Noh   +6 more
doaj   +1 more source

ROLE OF PHYSICS IN PHYSICS-INFORMED MACHINE LEARNING

Journal of Machine Learning for Modeling and Computing
Physical systems are characterized by inherent symmetries, one of which is encapsulated in the units of their parameters and system states. These symmetries enable a lossless order-reduction, e.g., via dimensional analysis based on the Buckingham theorem.
Chandra, Abhishek, Bakarji, Joseph, Tartakovsky, Daniel M.   +2 more
openaire   +2 more sources

Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential

FEBS Letters, EarlyView.
Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta, Hari Baskar Balasubramanian, Amir Pasokh, Marietta Zille   +3 more
wiley   +1 more source

Physics-informed features in supervised machine learning

CoRR
Supervised machine learning involves approximating an unknown functional relationship from a limited dataset of features and corresponding labels. The classical approach to feature-based machine learning typically relies on applying linear regression to standardized features, without considering their physical meaning.
Margherita Lampani, Sabrina Guastavino, Michele Piana, Federico Benvenuto   +3 more
openaire   +2 more sources

Structure‐forward targeting of claudins with synthetic binders

FEBS Letters, EarlyView.
Claudins form the paracellular barriers between epithelial and endothelial tissues at tight junctions and are targets for molecular binders with the goal of modulating barrier permeability. Claudin‐binding molecules are relevant in drug delivery or in altering claudin interactions with disease‐causing proteins.
Alex J. Vecchio
wiley   +1 more source

Hybrid physics-informed artificial intelligence for high-fidelity modeling and optimization of electrical systems

Frontiers in Artificial Intelligence
The integration of machine learning with domain-specific physics has revolutionized the design, monitoring, and control of electrical machines and drives.
Joseph Nyangon
doaj   +1 more source

Emerging experimental and computational methods for studying redox‐regulated structural transitions

FEBS Letters, EarlyView.
Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass, Dana Reichmann, Gábor Erdős   +2 more
wiley   +1 more source