Separable Hamiltonian neural networks.

Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations. A recent observation is that embedding a bias regarding the additive separability of the Hamiltonian reduces the regression complexity and improves regression performance. We propose separable HNNs that embed additive separability within HNNs using observational, learning, and inductive biases.

Life cycle assessment of single-use surgical and embedded filtration layer (EFL) reusable face mask.

Background: The outbreak of the COVID-19 pandemic has led to an unprecedented amount of face mask consumption around the world. The increase in face mask consumption has brought focus to their environmental impact. To keep up with the increased demand for face masks, different variations of reusable face masks such as the embedded filtration layer (EFL) reusable face mask have emerged in the market.