Results 41 to 50 of about 6,454,315 (251)

Directed evolution of enzymes at the crossroads of tradition and innovation

open access: yesFEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova   +2 more
wiley   +1 more source

MT-PINNs: multi-term physics-informed neural networks for solving initial boundary value problems of 2D and 3D nonlinear telegraph equations

open access: yesBoundary Value Problems
The nonlinear telegraph equation appears in a variety of engineering and science problems. This paper presents a deep learning algorithm termed multi-term physics-informed neural networks to resolve initial boundary value problems of 2D and 3D hyperbolic
Alemayehu Tamirie Deresse   +2 more
doaj   +1 more source

h-Analysis and data-parallel physics-informed neural networks

open access: yesScientific Reports, 2023
We explore the data-parallel acceleration of physics-informed machine learning (PIML) schemes, with a focus on physics-informed neural networks (PINNs) for multiple graphics processing units (GPUs) architectures. In order to develop scale-robust and high-
Paul Escapil-Inchauspé, Gonzalo A. Ruz
doaj   +1 more source

Comparing the Effect of Semi‐Immersive Virtual Reality, Computerized Cognitive Training, and Traditional Rehabilitation on Cognitive Function in Multiple Sclerosis: A Randomized Clinical Trial

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Cognitive impairment is a common non‐motor symptom in Multiple Sclerosis (MS), negatively affecting autonomy and Quality of Life (QoL). Innovative rehabilitation strategies, such as semi‐immersive virtual reality (VR) and computerized cognitive training (CCT), may offer advantages over traditional cognitive rehabilitation (TCR ...
Maria Grazia Maggio   +8 more
wiley   +1 more source

Theory-informed neural networks for particle physics

open access: yesMachine Learning: Science and Technology
We present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision process.
Barry M Dillon, Michael Spannowsky
doaj   +1 more source

Artificial Intelligence for Studying Interactions of Solitons and Peakons

open access: yesMathematics
In this paper, Artificial Intelligence (AI) is developed for studying the Boussinesq Paradigm equation and so called b-equation based on Physics-Informed Cellular Neural Networks (PICNNs). The models studied here come from fluid dynamics.
Angela Slavova, Ventsislav Ignatov
doaj   +1 more source

Evaluation of Digital Technologies for Home‐Based Assessment in People With Amyotrophic Lateral Sclerosis

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Digital technologies hold promise for transforming healthcare by enhancing personalized treatments and offer valuable opportunities to improve patient care. Here, we evaluated several novel, self‐administered, home‐based, digital endpoints for their association with corresponding conventional standard clinical measures (primary) in ...
Arne Mueller   +14 more
wiley   +1 more source

Sex‐Stratified Association of Regional Dopamine Transporter Binding With Disease Progression in Amyotrophic Lateral Sclerosis

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To clarify the clinical relevance of dopamine transporter single‐photon emission computed tomography (DAT‐SPECT) abnormalities in amyotrophic lateral sclerosis (ALS), with a prespecified focus on sex‐stratified associations with disease progression and short‐term prognosis.
Tomoya Kawazoe   +7 more
wiley   +1 more source

Dataset and supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks"

open access: yes, 2023
<p>Dataset and associated supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks".</p ...
Malik, Mehul   +3 more
core   +1 more source

Towards Stable Training of Complex-Valued Physics-Informed Neural Networks: A Holomorphic Initialization Approach

open access: yesMathematics
This work introduces a new initialization scheme for complex-valued layers in physics-informed neural networks that use holomorphic activation functions.
Andrei-Ionuț Mohuț, Călin-Adrian Popa
doaj   +1 more source

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