Next-Generation Simulation Illuminates Scientific Problems of Organised Complexity

• URL: http://arxiv.org/abs/2401.09851v4
• Date: Fri, 14 Jun 2024 11:31:43 GMT
• Title: Next-Generation Simulation Illuminates Scientific Problems of Organised Complexity
• Authors: Cheng Wang, Chuwen Wang, Wang Zhang, Shirong Zeng, Yu Zhao, Ronghui Ning, Changjun Jiang,
• Abstract summary: We revisit a classic classification of scientific problems and acknowledge that a series of unresolved problems remain. We focus on next-generation simulation (NGS), which can serve as a platform to integrate methods from different paradigms. We propose a methodology, sophisticated behavioural simulation (SBS), to realise it.
• Score: 14.665628508798319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As artificial intelligence becomes increasingly prevalent in scientific research, data-driven methodologies appear to overshadow traditional approaches in resolving scientific problems. In this Perspective, we revisit a classic classification of scientific problems and acknowledge that a series of unresolved problems remain. Throughout the history of researching scientific problems, scientists have continuously formed new paradigms facilitated by advances in data, algorithms, and computational power. To better tackle unresolved problems, especially those of organised complexity, a novel paradigm is necessitated. While recognising that the strengths of new paradigms have expanded the scope of resolvable scientific problems, we aware that the continued advancement of data, algorithms, and computational power alone is hardly to bring a new paradigm. We posit that the integration of paradigms, which capitalises on the strengths of each, represents a promising approach. Specifically, we focus on next-generation simulation (NGS), which can serve as a platform to integrate methods from different paradigms. We propose a methodology, sophisticated behavioural simulation (SBS), to realise it. SBS represents a higher level of paradigms integration based on foundational models to simulate complex systems, such as social systems involving sophisticated human strategies and behaviours. NGS extends beyond the capabilities of traditional mathematical modelling simulations and agent-based modelling simulations, and therefore, positions itself as a potential solution to problems of organised complexity in complex systems.

Related papers

• LLM-Augmented Agent-Based Modelling for Social Simulations: Challenges and Opportunities [0.0]
  Integrating large language models with agent-based simulations offers a transformational potential for understanding complex social systems. We explore architectures and methods to systematically develop LLM-augmented social simulations. We conclude that integrating LLMs with agent-based simulations offers a powerful toolset for researchers and scientists.
  arXiv  Detail & Related papers  (2024-05-08T08:57:54Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• On Robust Numerical Solver for ODE via Self-Attention Mechanism [82.95493796476767]
  We explore training efficient and robust AI-enhanced numerical solvers with a small data size by mitigating intrinsic noise disturbances. We first analyze the ability of the self-attention mechanism to regulate noise in supervised learning and then propose a simple-yet-effective numerical solver, Attr, which introduces an additive self-attention mechanism to the numerical solution of differential equations.
  arXiv  Detail & Related papers  (2023-02-05T01:39:21Z)
• SMT-based Weighted Model Integration with Structure Awareness [18.615397594541665]
  We develop an algorithm that combines SMT-based enumeration, an efficient technique in formal verification, with an effective encoding of the problem structure. This allows our algorithm to avoid generating redundant models, resulting in substantial computational savings.
  arXiv  Detail & Related papers  (2022-06-28T09:46:17Z)
• Simulation Intelligence: Towards a New Generation of Scientific Methods [81.75565391122751]
  "Nine Motifs of Simulation Intelligence" is a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery.
  arXiv  Detail & Related papers  (2021-12-06T18:45:31Z)
• Constructing Neural Network-Based Models for Simulating Dynamical Systems [59.0861954179401]
  Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
  arXiv  Detail & Related papers  (2021-11-02T10:51:42Z)
• An Extensible Benchmark Suite for Learning to Simulate Physical Systems [60.249111272844374]
  We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols. We propose four representative physical systems, as well as a collection of both widely used classical time-based and representative data-driven methods.
  arXiv  Detail & Related papers  (2021-08-09T17:39:09Z)
• Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond [114.39616146985001]
  In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of Bi-Level Optimization (BLO) Then we construct a value-function-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies.
  arXiv  Detail & Related papers  (2021-01-27T16:20:23Z)
• Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling [8.896888286819635]
  Systems exhibiting chaos are ubiquitous across Earth Sciences. System Identification remains a challenge in climate science. We consider a chaotic system - two-level Lorenz-96 - used as a benchmark model in the climate science literature.
  arXiv  Detail & Related papers  (2020-08-12T22:37:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.