Molecular dynamics (MD) shows a difference in the mechanisms of fracture of iron under
uniaxial and isotropic tension. Uniaxial tension leads to the formation of a complex …

Statistical model calibration is a framework for inference on unknown model parameters and
modeling discrepancy between simulation and experiment through an inverse method in the …

In immersed tunnels, the considerable settlement that can develop during their long-term
service period may induce structural damage that affects normal operations (ie …

While deep neural networks are highly performant and successful in a wide range of real-
world problems, estimating their predictive uncertainty remains a challenging task. To …

Identifying the mathematical relationships that best describe a dataset remains a very
challenging problem in machine learning, and is known as Symbolic Regression (SR). In …

Current progress in numerical simulations and machine learning allows one to apply
complex loading conditions for the identification of parameters in plasticity models. This …

In this work, we perform a comprehensive study of the dynamic deformation and fracture of
brass, including Taylor tests with classical and profiled cylinders and ball throwing …

In this work, we compare two methods of using artificial neural network (ANN) to determine
the optimal parameters of material model by results of high-speed plate impact experiments …

We introduce MESSY estimation, a Maximum-Entropy based Stochastic and Symbolic
densitY estimation method. The proposed approach recovers probability density functions …

Symbolic regression has multiple applications in data mining and scientific computing.
Genetic Programming (GP) is the mainstream method of solving symbolic regression …