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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Lattice Thermal Conductivity Prediction Using Symbolic Regression and Machine Learning
Not provided.
Establishing a data-driven deformation model for Tin using symbolic regression and genetic programming .
Abstract not provided.
Improved Plasticity and Damage Models By Symbolic Regression of Microscale Finite Element Simulations.
Abstract not provided.
A Data-driven Approach for Improving the Existing Gurson Material Damage Model Using Genetic Programming for Symbolic Regression.
Abstract not provided.
Uncertainty Quantification for Interpretable Constitutive Models using Genetic Programming based Symbolic Regression
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Establishing a data-driven strength model for 𝜷-tin by performing symbolic regression using genetic programming
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Characterizing the complex deformation behavior of Tin using genetic programming to perform symbolic regression
This is the seminar I will present at WCCM conference highlighting our latest research work on incorporating genetic programming to obtain data-driven strength models for complex materials.
Material-informed topology optimization via symbolic regression
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Formulation of Plasticity Models through Symbolic Regression
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Modeling Fatigue Crack Growth Using Symbolic Regression
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Symbolic Regression with Genetic Programming
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Bayesian Genetic Programming Based Symbolic Regression with Preferential Search
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Uncertainty Quantification for Interpretable Constitutive Models using Genetic Programming based Symbolic Regression
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Interpretability and Generalizability of Constitutive Models using Symbolic Regression
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A demonstration platform for deep symbolic regression
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Interpretable Data-driven Plasticity Models with Symbolic Regression.
Abstract not provided.