A Kolosov-Muskhelishvili informed neural network satisfies plane elasticity equations by construction, achieves sub-1% errors on benchmarks, and uses transfer learning to predict crack paths under multiple criteria with over 70% less training time.
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Framework using potential outcomes and within-treatment regression models to estimate plot-level SOC sequestration potentials from covariates and approximate optimal policies, demonstrated on California rangeland data where targeting low-baseline-SOC plots improves outcomes over uniform policies.
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Transfer-learned Kolosov-Muskhelishvili Informed Neural Networks for Fracture Mechanics
A Kolosov-Muskhelishvili informed neural network satisfies plane elasticity equations by construction, achieves sub-1% errors on benchmarks, and uses transfer learning to predict crack paths under multiple criteria with over 70% less training time.
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Estimating soil carbon sequestration potential and approximating optimal management policies
Framework using potential outcomes and within-treatment regression models to estimate plot-level SOC sequestration potentials from covariates and approximate optimal policies, demonstrated on California rangeland data where targeting low-baseline-SOC plots improves outcomes over uniform policies.