- author = "Alexander Lavin and Hector Zenil and Brooks Paige and David Krakauer and Justin Gottschlich and Tim Mattson and Anima Anandkumar and Sanjay Choudry and Kamil Rocki and Atilim G{\"{u}}nes Baydin and Carina Prunkl and Olexandr Isayev and Erik Peterson and Peter L. McMahon and Jakob Macke and Kyle Cranmer and Jiaxin Zhang and Haruko M. Wainwright and Adi Hanuka and Manuela Veloso and Samuel Assefa and Stephan Zheng and Avi Pfeffer",
- title = "Simulation Intelligence: Towards a New Generation of Scientific Methods",
- howpublished = "arXiv",
- year = "2021",
- volume = "abs/2112.03235",
- month = "6 " # dec,
- keywords = "genetic algorithms, genetic programming, Simulation, Artificial Intelligence, Machine Learning, Scientific Computing, Physics-infused ML, Inverse Design, Human-Machine Teaming, Optimization, Causality, Complexity, Open-endedness",
-
URL = "
https://arxiv.org/abs/2112.03235",
- eprint = "2112.03235",
- timestamp = "Mon, 03 Jan 2022 22:03:29 +0100",
-
biburl = "https://dblp.org/rec/journals/corr/abs-2112-03235.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
- size = "108 pages",
-
abstract = "The original Seven Motifs set forth a roadmap of
essential methods for the field of scientific
computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We
present the {"}Nine Motifs of Simulation
Intelligence{"}, a roadmap for the development and
integration of the essential algorithms necessary for a
merger of scientific computing, scientific simulation,
and artificial intelligence. We call this merger
simulation intelligence (SI), for short. We argue the
motifs of simulation intelligence are interconnected
and interdependent, much like the components within the
layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation
intelligence operating system stack (SI-stack) and the
motifs therein:
(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.",
-
notes = "Institute for Simulation Intelligence.
p32 'Generating agent-based model programs from scratch' agent-based modeling (ABM)",
