Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …

Prognostics and health management (PHM) plays a constructive role in the equipment's
entire life health service. It has long benefited from intensive research into physics modeling …

We propose two approaches of locally adaptive activation functions namely, layer-wise and
neuron-wise locally adaptive activation functions, which improve the performance of deep …

High-fidelity reconstruction of dynamic fluids from sparse multiview RGB videos remains a
formidable challenge, due to the complexity of the underlying physics as well as the severe …

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

Deep neural networks (DNN) can model nonlinear relations between physical quantities.
Those DNNs are embedded in physical systems described by partial differential equations …

A Transformer-based deep direct sampling method is proposed for electrical impedance
tomography, a well-known severely ill-posed nonlinear boundary value inverse problem. A …

Many problems in science and engineering can be represented by a set of partial differential
equations (PDEs) through mathematical modeling. Mechanism-based computation following …

Topology optimization has emerged as a popular approach to refine a component's design
and increase its performance. However, current state-of-the-art topology optimization …

Spontaneous parametric downconversion (SPDC) in quantum optics is an invaluable
resource for the realization of high-dimensional qudits with spatial modes of light. One of the …