Results 131 to 140 of about 24,324,331 (382)

Machine learning surrogate for 3D phase-field modeling of ferroelectric tip-induced electrical switching

npj Computational Materials
Phase-field modeling offers a powerful tool for investigating the electrical control of the domain structure in ferroelectrics. However, its broad application is constrained by demanding computational requirements, limiting its utility in inverse design ...
Kévin Alhada–Lahbabi, Damien Deleruyelle, Brice Gautier   +2 more
doaj   +1 more source

Cardiovascular Modeling With Adapted Parametric Inference

ESAIM: Proceedings and Surveys, 2018
Computational modeling of the cardiovascular system, promoted by the advance of fluid-structure interaction numerical methods, has made great progress towards the development of patient-specific numerical aids to diagnosis, risk prediction, intervention ...
Lucor Didier, Le Maître Olivier P.
doaj   +1 more source

Linking neurogenesis, oligodendrogenesis, and myelination defects to neurodevelopmental disruption in primary mitochondrial disorders

FEBS Letters, EarlyView.
Mitochondrial remodeling shapes neural and glial lineage progression by matching metabolic supply with demand. Elevated OXPHOS supports differentiation and myelin formation, while myelin compaction lowers mitochondrial dependence, revealing mitochondria as key drivers of developmental energy adaptation.
Sahitya Ranjan Biswas, Porter L. Tomsick, Alicia M. Pickrell, Paul D. Morton   +3 more
wiley   +1 more source

Creating a Real‐Time Capable Surrogate Model of a Wind Turbine Using Machine Learning

Wind Energy
Multibody simulation (MBS) is an established method for modeling wind turbines for purposes such as load calculation. While capable of modeling wind turbines at high levels of detail, large MBS models can be computationally expensive.
Stefan Witter, Jakob Roth, Ralf Schelenz, Georg Jacobs   +3 more
doaj   +1 more source

Automated ICRF heating surrogate modeling via machine learning [PDF]

EPJ Web of Conferences
This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications.
Sanchez-Villar Alvaro, Bertelli Nicola, Shiraiwa Syun’ichi, Bai Zhe, Perciano Talita, Bethel E. Wes, Hillairet Julien, Miller Joseph, Wallace Gregory M., Wright John C.   +9 more
doaj   +1 more source

Improving ecological niche models by data mining large environmental datasets for surrogate models [PDF]

, 2005
David Stockwell
openalex   +1 more source

AAA+ protein unfoldases—the Moirai of the proteome

FEBS Letters, EarlyView.
AAA+ unfoldases are essential molecular motors that power protein degradation and disaggregation. This review integrates recent cryo‐electron microscopy (cryo‐EM) structures and single‐molecule biophysical data to reconcile competing models of substrate translocation.
Stavros Azinas, Marta Carroni
wiley   +1 more source

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

npj Computational Materials
Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations.
Sk Md Ahnaf Akif Alvi, Mrinalini Mulukutla, Nicolás Flores, Danial Khatamsaz, Jan Janssen, Danny Perez, Douglas Allaire, Vahid Attari, Raymundo Arróyave   +8 more
doaj   +1 more source

DEVELOPMENT OF WATER QUALITY MATRIX THROUGH SURROGATE MODELING

EQA, 2018
This paper presents the outcome of a research project that was focused on the monitoring of surface water quality through the development of a correlation matrix.
Laura Kusari
doaj   +1 more source

Surrogate Model: EPREM Simulations of SEP Events

, 2023
Bala Poduval, Atılım Güneş Baydin, N. A. Schwadron   +2 more
openalex   +1 more source