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Mahdi Kooshkbaghi

Showing results (1-10 of 12) with videos related to

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Physical Review. E|January 21, 2023
Machine-learning-based data-driven discovery of nonlinear phase-field dynamicsElham Kiyani, Steven Silber, Mahdi Kooshkbaghi, et al.
The Journal of Physical Chemistry. A|April 27, 2016
Spectral Quasi-Equilibrium Manifold for Chemical KineticsMahdi Kooshkbaghi, Christos E Frouzakis, Konstantinos Boulouchos, et al.
Chaos (Woodbury, N.Y.)|May 3, 2020
Manifold learning for organizing unstructured sets of process observationsFelix Dietrich, Mahdi Kooshkbaghi, Erik M Bollt, et al.
Gene Therapy|June 28, 2021
Novel tool to quantify with single-cell resolution the number of incoming AAV genomes co-expressed in the mouse nervous systemCarola J Maturana, Jessica L Verpeut, Mahdi Kooshkbaghi, et al.
The Journal of Chemical Physics|August 3, 2014
The global relaxation redistribution method for reduction of combustion kineticsMahdi Kooshkbaghi, Christos E Frouzakis, Eliodoro Chiavazzo, et al.
Chaos (Woodbury, N.Y.)|February 5, 2020
Coarse-scale PDEs from fine-scale observations via machine learningSeungjoon Lee, Mahdi Kooshkbaghi, Konstantinos Spiliotis, et al.
Frontiers in Computational Neuroscience|June 13, 2020
Emergent Spaces for Coupled OscillatorsThomas N Thiem, Mahdi Kooshkbaghi, Tom Bertalan, et al.
Journal of Computational Physics|May 28, 2019
Manifold learning for parameter reductionAlexander Holiday, Mahdi Kooshkbaghi, Juan M Bello-Rivas, et al.
Genome Biology|April 16, 2022
MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effectAmmar Tareen, Mahdi Kooshkbaghi, Anna Posfai, et al.
PNAS Nexus|January 30, 2023
On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiabilityNikolaos Evangelou, Noah J Wichrowski, George A Kevrekidis, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Physical Review. E|January 21, 2023
Machine-learning-based data-driven discovery of nonlinear phase-field dynamicsElham Kiyani, Steven Silber, Mahdi Kooshkbaghi, et al.
The Journal of Physical Chemistry. A|April 27, 2016
Spectral Quasi-Equilibrium Manifold for Chemical KineticsMahdi Kooshkbaghi, Christos E Frouzakis, Konstantinos Boulouchos, et al.
Chaos (Woodbury, N.Y.)|May 3, 2020
Manifold learning for organizing unstructured sets of process observationsFelix Dietrich, Mahdi Kooshkbaghi, Erik M Bollt, et al.
Gene Therapy|June 28, 2021
Novel tool to quantify with single-cell resolution the number of incoming AAV genomes co-expressed in the mouse nervous systemCarola J Maturana, Jessica L Verpeut, Mahdi Kooshkbaghi, et al.
The Journal of Chemical Physics|August 3, 2014
The global relaxation redistribution method for reduction of combustion kineticsMahdi Kooshkbaghi, Christos E Frouzakis, Eliodoro Chiavazzo, et al.
Chaos (Woodbury, N.Y.)|February 5, 2020
Coarse-scale PDEs from fine-scale observations via machine learningSeungjoon Lee, Mahdi Kooshkbaghi, Konstantinos Spiliotis, et al.
Frontiers in Computational Neuroscience|June 13, 2020
Emergent Spaces for Coupled OscillatorsThomas N Thiem, Mahdi Kooshkbaghi, Tom Bertalan, et al.
Journal of Computational Physics|May 28, 2019
Manifold learning for parameter reductionAlexander Holiday, Mahdi Kooshkbaghi, Juan M Bello-Rivas, et al.
Genome Biology|April 16, 2022
MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effectAmmar Tareen, Mahdi Kooshkbaghi, Anna Posfai, et al.
PNAS Nexus|January 30, 2023
On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiabilityNikolaos Evangelou, Noah J Wichrowski, George A Kevrekidis, et al.
Pageof 2