Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information

Analyses of internal structures and defects in materials using physics-informed neural networks

(2022) Enrui Zhang et al.   Science Advances

Generative design, manufacturing, and molecular modeling of 3D architected materials based on natural language input

(2022) Yu-Chuan Hsu et al.   APL Materials

Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset

(2022) Hiba Kobeissi et al.   JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME

High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning

(2022) Zhenze Yang et al.   Small Methods

Hierarchical Multiresolution Design of Bioinspired Structural Composites Using Progressive Reinforcement Learning

(2022) Chi‐Hua Yu et al.   Advanced Theory and Simulations

Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks

(2022) Zhenze Yang et al.   npj Computational Materials

Machine Learning Force Fields

(2021) Oliver T. Unke et al.   CHEMICAL REVIEWS

A review on application of mechanical metamaterials for vibration control

(2021) Srajan Dalela et al.   MECHANICS OF ADVANCED MATERIALS AND STRUCTURES

Deep learning model to predict complex stress and strain fields in hierarchical composites

(2021) Zhenze Yang et al.   Science Advances

End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures

(2021) Zhenze Yang et al.   JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS

Highly accurate protein structure prediction with AlphaFold

(2021) John Jumper et al.   NATURE

Accurate prediction of protein structures and interactions using a three-track neural network

(2021) Minkyung Baek et al.   SCIENCE

Deep learning framework for material design space exploration using active transfer learning and data augmentation

(2021) Yongtae Kim et al.   npj Computational Materials

Words to Matter: De novo Architected Materials Design Using Transformer Neural Networks

(2021) Zhenze Yang et al.   Frontiers in Materials

Multiscale Modeling Meets Machine Learning: What Can We Learn?

(2020) Grace C. Y. Peng et al.   ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING

Machine learning for data-driven discovery in solid Earth geoscience

(2019) Karianne J. Bergen et al.   SCIENCE

Applications of machine learning in drug discovery and development

(2019) Jessica Vamathevan et al.   NATURE REVIEWS DRUG DISCOVERY

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

(2019) Frederic E. Bock et al.   Frontiers in Materials

Machine Learning for Fluid Mechanics

(2019) Steven L. Brunton et al.   Annual Review of Fluid Mechanics

Machine learning and the physical sciences

(2019) Giuseppe Carleo et al.   REVIEWS OF MODERN PHYSICS

Places: A 10 Million Image Database for Scene Recognition

(2018) Bolei Zhou et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

(2018) Han Zhang et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Deep Learning for Computer Vision: A Brief Review

(2018) Athanasios Voulodimos et al.   Computational Intelligence and Neuroscience

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Flexible mechanical metamaterials

(2017) Katia Bertoldi et al.   Nature Reviews Materials

Tools for the numerical solution of inverse problems in structural mechanics: review and research perspectives

(2016) Emilio Turco   European Journal of Environmental and Civil Engineering

Utilization of inverse approach in the design of materials over nano- to macro-scale

(2015) Vladan Mlinar   ANNALEN DER PHYSIK

Deep learning

(2015) Yann LeCun et al.   NATURE

Advances in natural language processing

(2015) J. Hirschberg et al.   SCIENCE