Items where Project is "SODA"

Andelfinger, Philipp and Kreikemeyer, Justin N. (2024) Automatic Gradient Estimation for Calibrating Crowd Models with Discrete Decision Making. In: 24th International Conference on Computational Science (ICCS 2024), 02-04 Jul 2024, Málaga, Spain. Proceedings, published by Springer Nature Switzerland, Cham, pp. 227-241.

Kreikemeyer, Justin N. and Andelfinger, Philipp and Uhrmacher, Adelinde M. (2024) Towards Learning Stochastic Population Models by Gradient Descent. In: 38th ACM SIGSIM Principles of Advanced Discrete Simulation (PADS 2024), 24-26 Jun 2024, Atlanta, Georgia, USA. Proceedings, published by Association for Computing Machinery (ACM), New York, NY USA, pp. 88-92.

Kreikemeyer, Justin N. (2023) Tensor-Based Smooth Execution of Stochastic Agent-Based Simulations. Masters thesis, Institute for Visual and Analytic Computing, University of Rostock.

Kreikemeyer, Justin N. and Andelfinger, Philipp (2023) Smoothing Methods for Automatic Differentiation Across Conditional Branches. IEEE Access, 11, pp. 143190-143211. ISSN 2169-3536.

Tan, Wen Jun and Andelfinger, Philipp and Cai, Wentong and Eckhoff, David and Knoll, Alois (2023) Spatial iterative coordination for parallel simulation-based optimization of large-scale traffic signal control. Simulation: Transactions of the Society for Modeling and Simulation International, ISSN 0037-5497 (online 1741-3133).

Trad, Lorka (2023) Characterizing Objective Functions in Simulation Optimization using Agent-Based Models. Bachelor thesis, Institute for Visual and Analytic Computing, University of Rostock.

Andelfinger, Philipp (2022) Towards Differentiable Agent-Based Simulation. ACM Transactions on Modeling and Computer Simulation, 32 (4), pp. 1-26. ISSN 1049-3301.

Beier, Mathes (2022) Differentiable Modeling of Traffic Signals for Gradient-based Parameter Optimization in Microscopic Traffic Simulations. Bachelor thesis, Institute for Visual and Analytic Computing, University of Rostock.

Andelfinger, Philipp (2021) Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization. In: 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS'21), 31 May - 02 June, Suffolk, Virginia, USA. Proceedings, published by Association for Computing Machinery (ACM), New York, NY, USA, pp. 27-38.