Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

Integrating physics models within machine learning models holds considerable promise
toward learning robust models with improved interpretability and abilities to extrapolate. In …

In this paper, we work towards a neural surrogate to model wireless electro-magnetic
propagation effects in indoor environments. Such neural surrogates provide a fast …

Neural networks are powerful surrogates for numerous forward processes. The inversion of
such surrogates is extremely valuable in science and engineering. The most important …

Mechanical meta-materials are solids whose geometric structure results in exotic nonlinear
behaviors that are not typically achievable via homogeneous materials. We show how to …

The goal in system-level design is to generate a diverse set of high-performing design
configurations that allow trade-offs across different objectives and avoid early concretization …

In 3D printing, stiff fibers (eg, carbon fiber) can reinforce thermoplastic polymers with limited
stiffness. However, existing commercial digital manufacturing software only provides a few …

In computational design and fabrication, neural networks are becoming important surrogates
for bulky forward simulations. A long-standing, intertwined question is that of inverse design …

Mechanical metamaterials are a special class of materials, whose mechanical properties are
primarily determined by their geometry and topology. Due to their unique properties and …

The design of complex physical systems entails satisfying several competing performance
objectives. In practice, some design requirements are often implicit in the intuition and …