Simulation Intelligence: Towards a New Generation of Scientific Methods (2112.03235v2)

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.

• The paper introduces a novel Simulation Intelligence framework that synergizes scientific computing, AI, and simulation to revolutionize method-driven discovery.
• It details nine algorithmic motifs, including multi-physics modeling and surrogate modeling, that collectively drive improvements in simulation accuracy and efficiency.
• The framework offers practical insights for advancing research in diverse fields such as climate science, synthetic biology, and socioeconomics.

Simulation Intelligence: Towards a New Generation of Scientific Methods

The paper "Simulation Intelligence: Towards a New Generation of Scientific Methods" introduces a sophisticated conceptual framework aiming to revolutionize scientific methodology through a robust combination of scientific computing, AI, and scientific simulation. Termed Simulation Intelligence (SI), this integration is encapsulated in a roadmap of nine foundational algorithmic methods that the authors call the Nine Motifs of Simulation Intelligence. The paper advocates for the synergy among these motifs, arguing that their collaborative leverage is essential for progressing scientific discovery in various domains, including but not limited to synthetic biology, climate science, and socioeconomic systems.

The Nine Motifs of Simulation Intelligence

The SI stack outlined in the paper is composed of nine interdependent motifs, each representing critical aspects of this new paradigm:

1. Multi-Physics and Multi-Scale Modeling: Enables the simulation of complex systems characterized by various interacting physical phenomena across different spatial and temporal scales. The integration of physics-informed ML models enhances the accuracy and reliability of these simulations.
2. Surrogate Modeling and Emulation: Focuses on computationally efficient models that approximate the behavior of expensive simulations. Approaches like Gaussian processes and neural network-based surrogates are pivotal in this context.
3. Simulation-Based Inference: Allows for statistical inference in settings where likelihoods are computationally prohibitive. This motif leverages recent advancements in deep learning to approximate posteriors and likelihood ratios, advancing traditional implicit models.
4. Causal Modeling and Inference: Develops methodologies to distinguish causal relationships from correlations in data. This motif uses counterfactual reasoning and structural causal models to address interventional and observational queries.
5. Agent-Based Modeling: Simulates dynamic systems using autonomous agents to explore emergent behaviors. Recent advances in multi-agent reinforcement learning (MARL) contribute to expanding the applicability of this motif.
6. Probabilistic Programming: Utilizes high-level programming languages to create complex probabilistic models where built-in inference engines are used for analysis. These languages facilitate the integration of domain knowledge and uncertainty quantification.
7. Differentiable Programming: Applies automatic differentiation to enable gradient-based optimization in algorithmic structures. Frameworks like JAX and DiffTaichi have been highlighted for their utility in end-to-end differentiable simulation workflows.
8. Open-Ended Optimization: Introduces algorithms that generate novelty and increase complexity rather than minimizing loss functions. This approach is vital for tasks requiring continual learning and adaptation.
9. Machine Programming: Automates the development of software and possibly hardware to maximize the performance and efficiency of SI workflows. This includes the use of program synthesis and formal methods.

Implications and Future Prospects

The combination of these nine motifs has immense potential for advancing scientific discovery. For instance, multi-physics and multi-scale modeling, when combined with probabilistic programming, can significantly improve the accuracy and reliability of simulations in climate science. Surrogate modeling integrated with open-ended optimization can accelerate materials discovery by exploring vast design spaces more efficiently than traditional methods.

Furthermore, probabilistic programming and causal modeling are crucial for deriving insights from large datasets, enabling researchers to establish causal relationships that guide experimental design and policy decisions. The mathematical foundation provided by differentiable programming ensures that these models are computationally efficient and adaptable to varying conditions in real-time.

The paper also considers the practical aspects of implementing SI, highlighting the necessity for advanced data engineering and high-performance computing infrastructure. A significant emphasis is placed on the challenges of data interoperability, readiness, and provenance, which are essential for ensuring the reproducibility and reliability of results in scientific research.

Conclusion

The paper posits that the intersection of AI, scientific computing, and scientific simulation constitutes a transformative approach to scientific inquiry. By articulating the Nine Motifs of Simulation Intelligence, the authors provide a comprehensive framework that harnesses the strengths of multiple advanced computational methodologies. The future of scientific discovery, as envisaged in this work, lies in leveraging these motifs synergistically to navigate complex, high-dimensional spaces of potential solutions, ultimately contributing to significant advancements in various scientific and engineering domains.