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Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations

Macromolecules, 2016-12, Vol.49 (24), p.9474-9483

2016

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Special issue of computational mechanics on machine learning theories, modeling, and applications to computational materials science, additive manufacturing, mechanics of materials, design and optimization

Computational mechanics, 2023-07, Vol.72 (1), p.1-2

2023

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Centimeter-scale nanomechanical resonators with low dissipation

Nature communications, 2024-05, Vol.15 (1), p.4255-4255, Article 4255

2024

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Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible

Advanced materials (Weinheim), 2019-11, Vol.31 (48), p.e1904845-n/a

2019

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High-fidelity micro-scale modeling of the thermo-visco-plastic behavior of carbon fiber polymer matrix composites

Composite structures, 2015-12, Vol.134 (C), p.132-141

2015

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Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning

Advanced materials (Weinheim), 2022-01, Vol.34 (3), p.e2106248-n/a

2022

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A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling

Composites science and technology, 2019-01, Vol.170 (C), p.15-24

2019

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High‐Strength Amorphous Silicon Carbide for Nanomechanics

Advanced materials (Weinheim), 2024-02, Vol.36 (5), p.e2306513-n/a

2024

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Continual prune-and-select: class-incremental learning with specialized subnetworks

Applied intelligence (Dordrecht, Netherlands), 2023-07, Vol.53 (14), p.17849-17864

2023

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Neural network relief: a pruning algorithm based on neural activity

Machine learning, 2024-05, Vol.113 (5), p.2597-2618

2024

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Quantifying nanoscale forces using machine learning in dynamic atomic force microscopy

Nanoscale advances, 2022-05, Vol.4 (9), p.2134-2143

2022

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Mechanics of hybrid polymer composites: analytical and computational study

Computational mechanics, 2016-03, Vol.57 (3), p.405-421

2016

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Continual learning for surface defect segmentation by subnetwork creation and selection

Journal of intelligent manufacturing, 2024-05

2024

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Cooperative data-driven modeling

Computer methods in applied mechanics and engineering, 2023-12, Vol.417, p.116432, Article 116432

2023

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High‐Strength Amorphous Silicon Carbide for Nanomechanics (Adv. Mater. 5/2024)

Advanced materials (Weinheim), 2024-02, Vol.36 (5), p.n/a

2024

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iPINNs: incremental learning for Physics-informed neural networks

Engineering with computers, 2024-06

2024

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Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning (Adv. Mater. 3/2022)

Advanced materials (Weinheim), 2022-01, Vol.34 (3), p.n/a

2022

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CRATE: A Python package to perform fast material simulations

Journal of open source software, 2023-07, Vol.8 (87), p.5594

2023

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Sparse quantum Gaussian processes to counter the curse of dimensionality

Quantum machine intelligence, 2021-06, Vol.3 (1), Article 6

2021

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Corrigendum to “High-fidelity micro-scale modeling of the thermo-visco-plastic behavior of carbon fiber polymer matrix composites” [Compos Struct 134 (2015) 132–141]

Composite structures, 2016-10, Vol.153 (C), p.724-727

2016

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Data-driven Multi-scale Analyses of Materials and Structures

Bessa, Miguel A

2016

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Particle Method Modeling of Nonlocal Multiresolution Continua

Meshfree Methods for Partial Differential Equations VII, 2014, p.43-60

2014

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Do Multisensory Stimuli Benefit the Virtual Reality Experience? A Systematic Review

IEEE transactions on visualization and computer graphics, 2022-02, Vol.28 (2), p.1428-1442

2022

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Teaching EFL With Immersive Virtual Reality Technologies: A Comparison With the Conventional Listening Method

IEEE access, 2023, Vol.11, p.21498-21507

2023

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TEAD and YAP regulate the enhancer network of human embryonic pancreatic progenitors

Nature cell biology, 2015-05, Vol.17 (5), p.615-626

2015

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Predicting the Macroscopic Fracture Energy of Epoxy Resins from Atomistic Molecular Simulations

Special issue of computational mechanics on machine learning theories, modeling, and applications to computational materials science, additive manufacturing, mechanics of materials, design and optimization

Centimeter-scale nanomechanical resonators with low dissipation

Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible

High-fidelity micro-scale modeling of the thermo-visco-plastic behavior of carbon fiber polymer matrix composites

Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning

A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling

High‐Strength Amorphous Silicon Carbide for Nanomechanics

Continual prune-and-select: class-incremental learning with specialized subnetworks

Neural network relief: a pruning algorithm based on neural activity

Quantifying nanoscale forces using machine learning in dynamic atomic force microscopy

Mechanics of hybrid polymer composites: analytical and computational study

Continual learning for surface defect segmentation by subnetwork creation and selection

Cooperative data-driven modeling

High‐Strength Amorphous Silicon Carbide for Nanomechanics (Adv. Mater. 5/2024)

iPINNs: incremental learning for Physics-informed neural networks

Spiderweb Nanomechanical Resonators via Bayesian Optimization: Inspired by Nature and Guided by Machine Learning (Adv. Mater. 3/2022)

CRATE: A Python package to perform fast material simulations

Sparse quantum Gaussian processes to counter the curse of dimensionality

Corrigendum to “High-fidelity micro-scale modeling of the thermo-visco-plastic behavior of carbon fiber polymer matrix composites” [Compos Struct 134 (2015) 132–141]

Data-driven Multi-scale Analyses of Materials and Structures

Particle Method Modeling of Nonlocal Multiresolution Continua

Do Multisensory Stimuli Benefit the Virtual Reality Experience? A Systematic Review

Teaching EFL With Immersive Virtual Reality Technologies: A Comparison With the Conventional Listening Method

TEAD and YAP regulate the enhancer network of human embryonic pancreatic progenitors

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