Identification and qualitative comparison of sensitivity analysis methods that have been used
across various disciplines, and that merit consideration for application to food‐safety risk …

Numerical models that combine weather forecasting and atmospheric chemistry are here
referred to as chemical weather forecasting models. Eighteen operational chemical weather …

Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine
learning. Automatic differentiation (AD), also called algorithmic differentiation or simply" auto …

We introduce JAX MD, a software package for performing differentiable physics simulations
with a focus on molecular dynamics. JAX MD includes a number of statistical physics …

The following techniques for uncertainty and sensitivity analysis are briefly summarized:
Monte Carlo analysis, differential analysis, response surface methodology, Fourier …

In every decision problem there are things we know and things we do not know. Risk
analysis science uses the best available evidence to assess what we know while it is …

Abstract Characterization of uncertainty associated with transport-transformation models is
often of critical importance, as for example in cases where environmental and biological …

Efficient prediction of the air quality response to emission changes is a prerequisite for an
integrated assessment system in developing effective control policies. Yet, representing the …

The task of providing an optimal analysis of the state of the atmosphere requires the
development of efficient computational tools that facilitate an efficient integration of …

A direct sensitivity analysis technique is extended to calculate higher-order sensitivity
coefficients in three-dimensional air quality models. The time evolution of sensitivity …