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Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/adts.201900056openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.drudis.2014.10.012openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eml.2024.102269openalex-snapshot
Automated de novo design of architectured materials: Leveraging eXplainable Artificial Intelligence (XAI) for inspiration from stochastic microstructure outliers
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106267openalex-snapshot
Data-driven methods for stress field predictions in random heterogeneous materials
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.107955openalex-snapshot
Optimization of spatial mode separation in few-mode nanostructured fibers with generative inverse design networks
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.110473openalex-snapshot
Navigating beyond the training set: A deep learning framework for inverse design of architected composite materials
inferred← citeshttps://doi.org/10.1016/j.engfracmech.2022.108471openalex-snapshot
Mechanical properties and healing efficiency of 3D-printed ABS vascular based self-healing cementitious composite: Experiments and modelling
inferred← citeshttps://doi.org/10.1016/j.engfracmech.2025.111778openalex-snapshot
Machine learning for computational fracture and damage mechanics— Status and perspectives
inferred← citeshttps://doi.org/10.1016/j.engstruct.2024.118079openalex-snapshot
Inverse machine learning framework for optimizing gradient honeycomb structure under impact loading
inferred← citeshttps://doi.org/10.1016/j.engstruct.2024.119541openalex-snapshot
Pareto-guided active learning for accelerating surrogate-assisted multi-objective optimization of arch dam shape
inferred← citeshttps://doi.org/10.1016/j.ensm.2024.103612openalex-snapshot
AI for dielectric capacitors
inferred← citeshttps://doi.org/10.1016/j.envres.2022.113953openalex-snapshot
Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water
inferred← citeshttps://doi.org/10.1016/j.est.2026.121956openalex-snapshot
Artificial intelligence techniques in energy-storage polymer-based dielectrics: A review
inferred← citeshttps://doi.org/10.1016/j.ijadhadh.2024.103684openalex-snapshot
Biomimicry and topology optimization for adhesive toughness design in bonded heterogeneous films
inferred← citeshttps://doi.org/10.1016/j.ijfatigue.2025.109347openalex-snapshot
Physics-informed neural network-based life prediction for copper alloy under low-cycle fatigue and creep-fatigue interaction
inferred← citeshttps://doi.org/10.1016/j.ijheatmasstransfer.2021.122313openalex-snapshot
An adaptive artificial neural network-based generative design method for layout designs
inferred← citeshttps://doi.org/10.1016/j.ijimpeng.2025.105443openalex-snapshot
Deep learning accelerating design of functionally graded materials for application in dynamic loading
inferred← citeshttps://doi.org/10.1016/j.ijlmm.2021.04.001openalex-snapshot
Carbon-fiber reinforced polymer composites: A comparison of manufacturing methods on mechanical properties
inferred← citeshttps://doi.org/10.1016/j.ijmecsci.2024.109331openalex-snapshot
Deep learning of buckling instability in geometrically symmetry-breaking kirigami
inferred← citeshttps://doi.org/10.1016/j.ijmecsci.2024.109393openalex-snapshot
An automated design framework for composite mechanical metamaterials and its application to 2D pentamode materials
inferred← citeshttps://doi.org/10.1016/j.ijmecsci.2024.109736openalex-snapshot
Deep-learning-based generative design for optimal reactive silencers
inferred← citeshttps://doi.org/10.1016/j.ijmecsci.2025.110297openalex-snapshot
Progressive DNN-metaheuristics for programmable broadband mechanical metamaterial absorbers design
inferred← citeshttps://doi.org/10.1016/j.ijmecsci.2025.110797openalex-snapshot
A generative framework for structural crack discovery under limited sensing
inferred← citeshttps://doi.org/10.1016/j.ijmecsci.2025.110835openalex-snapshot
Data-driven inverse design of C/C honeycombs via multiscale damage modeling
inferred← citeshttps://doi.org/10.1016/j.ijmecsci.2026.112110openalex-snapshot
Machine learning-enabled inverse design of heterogeneous impact-resistant metamaterials
inferred← citeshttps://doi.org/10.1016/j.ijplas.2020.102852openalex-snapshot
Deep learning for plasticity and thermo-viscoplasticity
inferred← citeshttps://doi.org/10.1016/j.jcomc.2024.100529openalex-snapshot
Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures
inferred← citeshttps://doi.org/10.1016/j.jconrel.2022.12.029openalex-snapshot
Colonic drug delivery: Formulating the next generation of colon-targeted therapeutics
inferred← citeshttps://doi.org/10.1016/j.jcp.2020.110072openalex-snapshot
Learning constitutive relations using symmetric positive definite neural networks
inferred← citeshttps://doi.org/10.1016/j.jcp.2021.110839openalex-snapshot
The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity
inferred← citeshttps://doi.org/10.1016/j.jcp.2023.112008openalex-snapshot
On the influence of over-parameterization in manifold based surrogates and deep neural operators
inferred← citeshttps://doi.org/10.1016/j.jelechem.2025.119150openalex-snapshot
Machine learning-driven prediction and optimization of selective glycerol electrocatalytic reduction into propanediols
inferred← citeshttps://doi.org/10.1016/j.jmapro.2021.05.011openalex-snapshot
Laser scan strategy descriptor for defect prognosis in metal additive manufacturing using neural networks
inferred← citeshttps://doi.org/10.1016/j.jmat.2023.05.001openalex-snapshot
Generative artificial intelligence and its applications in materials science: Current situation and future perspectives
inferred← citeshttps://doi.org/10.1016/j.jmmm.2024.171937openalex-snapshot
Image-based prediction and optimization of hysteresis properties of nanocrystalline permanent magnets using deep learning
inferred← citeshttps://doi.org/10.1016/j.jmmm.2025.173566openalex-snapshot
Determining the anisotropy field of permanent magnets: A comparison of current methodologies
inferred← citeshttps://doi.org/10.1016/j.jmps.2024.105857openalex-snapshot
Data-driven bio-mimetic composite design: Direct prediction of stress–strain curves from structures using cGANs
inferred← citeshttps://doi.org/10.1016/j.jmrt.2022.10.153openalex-snapshot
Data-driven compressive strength prediction of steel fiber reinforced concrete (SFRC) subjected to elevated temperatures using stacked machine learning algorithms
inferred← citeshttps://doi.org/10.1016/j.jnca.2024.103903openalex-snapshot
Enhanced detection of low-rate DDoS attack patterns using machine learning models
inferred← citeshttps://doi.org/10.1016/j.jnca.2026.104441openalex-snapshot
Generalized detection of DDoS attack patterns using machine learning models
inferred← citeshttps://doi.org/10.1016/j.jobe.2025.113577openalex-snapshot
Shear strength of steel fiber reinforced concrete under compression-shear loading at elevated temperature: Experimental, numerical, and machine learning investigation
inferred← citeshttps://doi.org/10.1016/j.matchar.2022.111759openalex-snapshot
Austenitic parent grain reconstruction in martensitic steel using deep learning
inferred← citeshttps://doi.org/10.1016/j.matdes.2020.109098openalex-snapshot
Deep learning for topology optimization of 2D metamaterials
inferred← citeshttps://doi.org/10.1016/j.matdes.2021.109632openalex-snapshot
Pragmatic generative optimization of novel structural lattice metamaterials with machine learning
inferred← citeshttps://doi.org/10.1016/j.matdes.2021.109937openalex-snapshot
Inverse machine learning framework for optimizing lightweight metamaterials
inferred← citeshttps://doi.org/10.1016/j.matdes.2022.111223openalex-snapshot
Harnessing structural stochasticity in the computational discovery and design of microstructures
inferred← citeshttps://doi.org/10.1016/j.matdes.2024.113189openalex-snapshot
Decoupling stiffness and peak moment via hierarchical snapping structures designed with machine learning
inferred← citeshttps://doi.org/10.1016/j.matdes.2024.113376openalex-snapshot
An artifactual fibre overlap removal algorithm for micro-computed tomography image post-processing and 3D microstructure generation with graphics processing unit acceleration
inferred← citeshttps://doi.org/10.1016/j.matdes.2024.113377openalex-snapshot
Customizable metamaterial design for desired strain-dependent Poisson’s ratio using constrained generative inverse design network
inferred← citeshttps://doi.org/10.1016/j.matdes.2024.113470openalex-snapshot
Productive automation of calibration processes for crystal plasticity model parameters via reinforcement learning
inferred← citeshttps://doi.org/10.1016/j.matre.2021.100047openalex-snapshot
Computational discovery of energy materials in the era of big data and machine learning: A critical review
inferred← citeshttps://doi.org/10.1016/j.matt.2020.08.023openalex-snapshot
Machine Learning for Advanced Additive Manufacturing
inferred← citeshttps://doi.org/10.1016/j.mechmat.2023.104778openalex-snapshot
Designing directional adhesive pillars using deep learning-based optimization, 3D printing, and testing
inferred← citeshttps://doi.org/10.1016/j.mechmat.2024.105163openalex-snapshot
Quantification of Elastic Incompatibilities at Triple Junctions Via Physics-Based Surrogate Models
inferred← citeshttps://doi.org/10.1016/j.mser.2023.100746openalex-snapshot
Recent applications of machine learning in alloy design: A review
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2023.107439openalex-snapshot
A deep learning-based approach for the inverse design of the Helmholtz resonators
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2025.112629openalex-snapshot
Inverse design of GaN HEMT heterostructures via deep learning with enhanced auto-encoder framework
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2025.113087openalex-snapshot
Variational autoencoder guided conditional diffusion generative model for material microstructure reconstruction and inverse design
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2026.115625openalex-snapshot
Modeling and investigation of energy absorbing capacity of NiTi-carbon fiber hybrid composite for automotive bumper application
inferred← citeshttps://doi.org/10.1016/j.neunet.2025.108040openalex-snapshot
Exploring the mechanical behavior of friction material composites using artificial intelligence
inferred← citeshttps://doi.org/10.1016/j.nxmate.2024.100449openalex-snapshot
Machine learning applied to the design and optimization of polymeric materials: A review
inferred← citeshttps://doi.org/10.1016/j.optcom.2020.126641openalex-snapshot
Simultaneous inverse design continuous and discrete parameters of nanophotonic structures via back-propagation inverse neural network
inferred← citeshttps://doi.org/10.1016/j.optcom.2022.128155openalex-snapshot
Fast inverse design of nanophotonics using differential evolution and back-propagation
inferred← citeshttps://doi.org/10.1016/j.pmatsci.2023.101132openalex-snapshot
Mechanical energy metamaterials in interstellar travel
inferred← citeshttps://doi.org/10.1016/j.tafmec.2021.103213openalex-snapshot
A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification
inferred← citeshttps://doi.org/10.1016/j.taml.2020.11.001openalex-snapshot
DUPLICATE: Physics-informed deep learning for digital materials
inferred← citeshttps://doi.org/10.1016/j.taml.2021.100220openalex-snapshot
Physics-informed deep learning for digital materials
inferred← citeshttps://doi.org/10.1016/j.tifs.2023.06.005openalex-snapshot
Inverse design and AI/Deep generative networks in food design: A comprehensive review
inferred← citeshttps://doi.org/10.1016/j.tws.2021.107810openalex-snapshot
Energy absorption of additively manufactured functionally bi-graded thickness honeycombs subjected to axial loads
inferred← citeshttps://doi.org/10.1016/j.tws.2023.110794openalex-snapshot
Graded honeycombs with high impact resistance through machine learning-based optimization
inferred← citeshttps://doi.org/10.1016/j.tws.2024.112718openalex-snapshot
Machine learning-based multi-objective optimization of thermo-mechanical field of anisotropic plates
inferred← citeshttps://doi.org/10.1016/j.tws.2025.113914openalex-snapshot
Applications of Machine Learning in Analysis and Design of Aerospace Composite Structures
inferred← citeshttps://doi.org/10.1016/j.tws.2025.114060openalex-snapshot
Height-independent optimal gradients in honeycombs boost energy absorption through delocalized deformation mechanism
inferred← citeshttps://doi.org/10.1016/j.xcrp.2024.102116openalex-snapshot
Advancements and prospects of deep learning in biomaterials evolution
inferred← citeshttps://doi.org/10.1016/s1003-6326(24)66672-0openalex-snapshot
Predictor−corrector inverse design scheme for property−composition prediction of amorphous alloys
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