Hybrid modeling architectures seek to combine a machine learning model with a
computationally efficient (simplified or partial) physics model to predict the behavior of …

A finite element (FE)–guided mathematical surrogate modeling methodology is presented
for evaluating relative injury trends across varied vehicular impact conditions. The …

Building sector energy consumption represents a significant fraction of the overall energy
consumption in urban communities. While there has been increasing focus on the …

Opportunistic physics-mining transfer mapping architecture (OPTMA) is a hybrid architecture
that combines fast simplified physics models with neural networks in order to provide …

View Video Presentation: https://doi. org/10.2514/6.2022-0384. vid Physics-Infused Machine
Learning (PIML) architectures aim at integrating machine learning with computationally …

Bioinspired surface riblets have been known to improve drag performance by altering the
near-wall flow structures, especially in the transitional flow regime. Unlike conventional riblet …

In surrogate-based optimization (SBO), the deception issues associated with the low fidelity
of the surrogate model can be dealt with in situ model refinement that uses infill points …

This paper proposes a novel and flexible J-type air-based battery thermal management
system (BTMS), by integrating conventional Z-type and U-type BTMS. With two controlling …

This paper presents an advancement of a model-independent surrogate based optimization
method with adaptive sampling, known as Adaptive Model Refinement. The primary …

View Video Presentation: https://doi. org/10.2514/6.2023-4376. vid Complex optimal design
and control processes often require repeated evaluations of expensive objective functions …