Created by W.Langdon from gp-bibliography.bib Revision:1.8081
(1) Multi-physics and multi-scale modeling;
(2) Surrogate modeling and emulation; (3) Simulation-based inference;
(4) Causal modeling and inference;
(5) Agent-based modeling; (6) Probabilistic programming;
(7) Differentiable programming; (8) Open-ended optimization;
(9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.",
p32 'Generating agent-based model programs from scratch' agent-based modeling (ABM)",
Genetic Programming entries for Alexander Lavin Hector Zenil Brooks Paige David Krakauer Justin Gottschlich Tim Mattson Anima Anandkumar Sanjay Choudry Kamil Rocki Atilim G\"unes Baydin Carina Prunkl Olexandr Isayev Erik Peterson Peter L McMahon Jakob Macke Kyle S Cranmer Jiaxin Zhang Haruko M Wainwright Adi Hanuka Manuela Veloso Samuel Assefa Stephan Zheng Avi Pfeffer