Cellular Automata

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Learning Traffic Flow Dynamics with Stochastic Physics-Informed Neural Cellular Automata
Oct 7, 2026Federica Bragone, Matthieu BarreauNeural Cellular Automata Cellular Automata
Department of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden
Spatial Induction Heads: In-Context Learning of Multidimensional Cellular Automata
Oct 6, 2026Kimia Kazemian, Menghan Xu, John Thickstun +1In-Context Learning Induction Heads
Cornell University
Where Does Randomness Matter in Neural Cellular Automata?
Sep 29, 2026Fei Zuo, Jiaqi Shi, Yujing LiuNeural Cellular Automata Cellular Automata
Fudan University · Independent Researcher · Shanghai Pupuda Culture Communication Co., Ltd.
Self-Replicating Neural Cellular Automata: Quantifying Emergent Phenotypic and Genotypic Diversity in an OpenEnded Substrate
Sep 17, 2026Sanyam Jain, Felix Simon Reimers, Stefano NicheleNeural Cellular Automata Artificial Life
Department of Computer Science and Communication, Østfold University of Applied Sciences, B R A veien 4, 1757 Halden, Norway · Department of Computer Science, Oslo Metropolitan University, Pilestredet 35, 0166 Oslo, Norway
Random Forest-Informed Cellular Automaton for Large-Scale Wildfire Spread Modelling
Sep 1, 2026Siyu Chen, Esha Saha, Hao WangCellular Automata Wildfire Forecasting
It's Much Easier for Neural Networks to learn Game of Life Dynamics with the Right Activation Function: Polynomial Kolmogorov-Arnold Networks
Jun 22, 2026Tashin Ahmed, Q. Tyrell DavisPolynomial Neural Networks Cellular Automata
Independent Researcher · Work completed while at Cross Labs, Cross Compass Ltd., Japan.
The Artificial Experimentalist: Discovery and Control of Self-Organizing Phenomena with Autotelic Reinforcement Learning
Jun 10, 2026Marko Cvjetko, Benedikt Hartl, Michael Levin +2Agentic RL Reinforcement Learning
Inria Centre at the University of Bordeaux, Bordeaux, France · Allen Discovery Center at Tufts University, Medford, MA, USA · Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA +1
Neural-Parameterized Cellular Automata for Wildfire Spread
Jun 10, 2026Maksym Zhenirovskyy, Ion Matei, Rohit Vuppala +2Neural Cellular Automata Cellular Automata
Fujitsu Research of America, 4655 Great America Pkwy, Santa Clara, 95124, CA, USA
Agnosiophobia in a virtual agent: behavioral and dynamical architecture in Lenia
May 29, 2026Jesse Cool, Benedikt Hartl, Michael Levin +1Cellular Automata Agent-Based Modeling
Department of Mathematics, Tufts University · Allen Discovery Center, Tufts University · Wyss Institute for Biologically Inspired Engineering, Harvard University
Von Neumann Networks
May 7, 2026Shekhar S. ChandraNeural Cellular Automata Cellular Automata
School of Electrical Engineering and Computer Science, The University of Queensland, Australia.
A New Kind of Network? Review and Reference Implementation of Neural Cellular Automata
Apr 27, 2026Martin Spitznagel, Janis KeuperNeural Cellular Automata Cellular Automata
Institute for Machine Learning and Analytics (IMLA), Offenburg University, Germany · University of Mannheim, Germany
Programmable Cellular Automata
Date pendingAhmed Khalifa, Muhammad Umair Nasir, Matthew Siper +2Cellular Automata Generative Modeling
Institute of Digital Games, University of Malta, 20 L. Esperanto, L-Imsida, Malta · Nof1, 665 Broadway, Brooklyn, NY 10012, USA · University of the Witwatersrand, 1 Jan Smuts Ave, Braamfontein, Johannesburg, 2017, South Africa +1

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Learning Traffic Flow Dynamics with Stochastic Physics-Informed Neural Cellular Automata

Federica Bragone, Matthieu Barreau

Department of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden

Neural Cellular Automata Cellular Automata Intelligent Transportation Systems

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Traffic flow modeling is essential for understanding and predicting the collective dynamics of vehicles on road networks. Cellular automata provide a simple, interpretable yet powerful framework for representing these dynamics via local interaction rules, while retaining the ability to reproduce complex macroscopic traffic phenomena. However, learning local transition rules from data while preserving physically meaningful constraints remains challenging, particularly for stochastic models. In this work, we propose a physics-informed neural cellular automaton (PI-NCA) for data-driven traffic flow modeling. Building on the standard neural cellular automaton (NCA), we design a neural architecture that is physically consistent with the road topology and guarantees conservation of the total number of vehicles, thereby constraining the learned transition rules to physically admissible dynamics. We further extend this framework to stochastic dynamics by parameterizing probabilistic transition rules while preserving the same physics-informed constraints. We evaluate the proposed models on multiple traffic scenarios generated by the well-established Nagel-Schreckenberg and Kerner-Klenov-Wolf cellular automata. The results demonstrate that the PI-NCA successfully learns the dynamics of both traffic models and consistently outperforms a standard NCA, while the stochastic extension captures probabilistic transition rules without compromising the imposed physical constraints.

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