In the rapidly evolving domain of machine learning, the ability to adapt to unforeseen
circumstances and novel data types is of paramount importance. The deployment of Artificial …

Accurately detecting crack boundaries is crucial for reliability assessment and risk
management of structures and materials, such as structural health monitoring, diagnostics …

Probabilistic safety assessment (PSA) is a tool for securing the operational safety of nuclear
power plants. One unique benefit of PSA is to identify accident scenarios leading to …

A global–local attention-based feature reconstruction (GLAFR) surrogate model is proposed
for uncertainty analysis (UA) in ground penetrating radar (GPR) simulation. The uncertain …

Reliable uncertainty quantification (UQ) in machine learning (ML) regression tasks is
becoming the focus of many studies in materials and chemical science. It is now well …

Actuaries use predictive modeling techniques to assess the loss cost on a contract as a
function of observable risk characteristics. State-of-the-art statistical and machine learning …

Learning from imperfect demonstrations is a crucial challenge in imitation learning (IL).
Unlike existing works that still rely on the enormous effort of expert demonstrators, we …

In this work, we developed a framework for identifying frame-type structures regarding the
measurement uncertainty and the uncertainty involved in inherent and structural parameters …

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

We introduce an Invertible Symbolic Regression (ISR) method. It is a machine learning
technique that generates analytical relationships between inputs and outputs of a given …