AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge

• URL: http://arxiv.org/abs/2504.01538v1
• Date: Wed, 02 Apr 2025 09:25:34 GMT
• Title: AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge
• Abstract summary: We propose AI-Newton, a concept-driven discovery system capable of autonomously deriving physical laws from raw data.<n>As a proof of concept, we apply AI-Newton to a large set of Newtonian mechanics problems.<n>This achievement marks a significant step toward AI-driven autonomous scientific discovery.
• Score: 4.180458188910334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current limitations in human scientific discovery necessitate a new research paradigm. While advances in artificial intelligence (AI) offer a highly promising solution, enabling AI to emulate human-like scientific discovery remains an open challenge. To address this, we propose AI-Newton, a concept-driven discovery system capable of autonomously deriving physical laws from raw data -- without supervision or prior physical knowledge. The system integrates a knowledge base and knowledge representation centered on physical concepts, along with an autonomous discovery workflow. As a proof of concept, we apply AI-Newton to a large set of Newtonian mechanics problems. Given experimental data with noise, the system successfully rediscovers fundamental laws, including Newton's second law, energy conservation and law of gravitation, using autonomously defined concepts. This achievement marks a significant step toward AI-driven autonomous scientific discovery.

Related papers

• Can AI Follow In Einstein's Footsteps? [4.43316916502814]
  We review the current landscape of AI for physics discovery.<n>We highlight a critical missing skill: the ability to pose the right questions or invent the right principles.
  arXiv  Detail & Related papers  (2026-07-30T07:27:07Z)
• Towards autonomous quantum physics research using LLM agents with access to intelligent tools [0.19116784879310023]
  We present AI-Mandel, an agent that can generate and implement ideas in quantum physics.<n> AI-Mandel formulates ideas from the literature and uses a domain-specific AI tool to turn them into concrete experiment designs.<n>The ideas include new variations of quantum teleportation, primitives of quantum networks in indefinite causal orders, and new concepts of geometric phases.
  arXiv  Detail & Related papers  (2025-11-13T18:18:58Z)
• Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI [57.44526951497041]
  We advocate for intelligent systems that ground learning in both physical principles and embodied reasoning processes.<n>Our synthesis envisions next-generation world models capable of explaining physical phenomena and predicting future states.
  arXiv  Detail & Related papers  (2025-10-06T16:16:03Z)
• Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI [98.19195693735487]
  We propose the paradigm of Intelligent Science Laboratories (ISLs)<n>ISLs are a multi-layered, closed-loop framework that deeply integrates cognitive and embodied intelligence.<n>We argue that such systems are essential for overcoming the current limitations of scientific discovery.
  arXiv  Detail & Related papers  (2025-06-24T13:31:44Z)
• AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research [58.944125758758936]
  The Science of Science (SoS) explores the mechanisms underlying scientific discovery.<n>The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS.<n>We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them.
  arXiv  Detail & Related papers  (2025-05-17T15:01:33Z)
• Digital Gene: Learning about the Physical World through Analytic Concepts [54.21005370169846]
  AI systems still struggle when it comes to understanding and interacting with the physical world. This research introduces the idea of analytic concept. It provides machine intelligence a portal to perceive, reason about, and interact with the physical world.
  arXiv  Detail & Related papers  (2025-04-05T13:22:11Z)
• Scaling Laws of Scientific Discovery with AI and Robot Scientists [72.3420699173245]
  We envision an autonomous generalist scientist (AGS) system-a fusion of agentic AI and embodied robotics.<n>By embedding advanced AI and robot technologies into every phase-from hypothesis formulation to peer-ready manuscripts-AGS could slash the time and resources needed for scientific research.<n>We foresee a future where scientific discovery follows new scaling laws, driven by the proliferation and sophistication of such systems.
  arXiv  Detail & Related papers  (2025-03-28T14:00:27Z)
• Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain [20.956507640605093]
  One fundamental question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI.
  arXiv  Detail & Related papers  (2024-12-04T13:17:44Z)
• Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence [0.9065034043031668]
  We argue that human complex reasoning for scientific discovery remains of vital importance, at least before the advent of artificial general intelligence.<n>Knowing the principles' the AI systems used to make decisions can be a point of contact with domain experts and scientists.<n>We define this field as Explainable AI for Science, where domain experts formulate scientific hypotheses and explanations based on the interpretability of a predictive AI system.
  arXiv  Detail & Related papers  (2024-06-15T08:34:42Z)
• AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
  Mathematics is one of the most powerful conceptual systems developed and used by the human species. Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
  arXiv  Detail & Related papers  (2023-10-19T02:00:31Z)
• DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies [116.09762105379241]
  DeepSpeed4Science aims to build unique capabilities through AI system technology innovations. We showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.
  arXiv  Detail & Related papers  (2023-10-06T22:05:15Z)
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [268.585904751315]
  New area of research known as AI for science (AI4Science)<n>Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales.<n>Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
  arXiv  Detail & Related papers  (2023-07-17T12:14:14Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems [5.7923858184309385]
  The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents. We will present closed-loop scientific discovery systems, starting with the pioneering work on the Adam system up to current efforts in fields from material science to astronomy. The maximal level, level five, is defined to require no human intervention at all in the production of scientific knowledge.
  arXiv  Detail & Related papers  (2023-05-03T16:35:41Z)
• From Kepler to Newton: Explainable AI-based Paradigm for Science Discovery [16.392568986688595]
  We introduce an Explainable AI-based paradigm for science discovery. To demonstrate the AI-based science discovery process, we show how Kepler's laws of planetary motion and the Newton's law of universal gravitation can be rediscovered by (Explainable) AI.
  arXiv  Detail & Related papers  (2021-11-24T00:45:27Z)

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.