5 papers
Learning surrogate equations for the analysis of an agent-based cancer model
Kevin Burrage, Pamela M. Burrage, Justin N. Kreikemeyer +2
In this paper, we adapt a two-species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction wi…
Using (Not-so) Large Language Models to Generate Simulation Models in a Formal DSL: A Study on Reaction Networks
Justin N. Kreikemeyer, Miłosz Jankowski, Pia Wilsdorf +1
Formal languages are an integral part of modeling and simulation. They allow the distillation of knowledge into concise simulation models amenable to automatic execution, interpret…
Automatic Gradient Estimation for Calibrating Crowd Models with Discrete Decision Making
Philipp Andelfinger, Justin N. Kreikemeyer
Recently proposed gradient estimators enable gradient descent over stochastic programs with discrete jumps in the response surface, which are not covered by automatic differentiati…
Towards Learning Stochastic Population Models by Gradient Descent
Justin N. Kreikemeyer, Philipp Andelfinger, Adelinde M. Uhrmacher
Increasing effort is put into the development of methods for learning mechanistic models from data. This task entails not only the accurate estimation of parameters but also a suit…
Smoothing Methods for Automatic Differentiation Across Conditional Branches
Justin N. Kreikemeyer, Philipp Andelfinger
Programs involving discontinuities introduced by control flow constructs such as conditional branches pose challenges to mathematical optimization methods that assume a degree of s…