Interactive symbolic regression with co-design mechanism through offline reinforcement learning. [PDF]
Nat CommunSymbolic Regression holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possible expressions poses significant challenges for previous online search methods ...
Tian Y +5 more
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Learning interpretable network dynamics via universal neural symbolic regression [PDF]
Nature CommunicationsDiscovering governing equations of complex network dynamics is a fundamental challenge in contemporary science with rich data, which can uncover the hidden patterns and mechanisms of the formation and evolution of complex phenomena in various fields and ...
Jiao Hu, Jiaxu Cui, Bo Yang
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SR-LLM: An incremental symbolic regression framework driven by LLM-based retrieval-augmented generation. [PDF]
Proc Natl Acad Sci U S ASignificance Scientists have long sought to derive models from extensive observational input–output data, ensuring these models accurately capture the underlying mapping from inputs to outputs while remaining interpretable to humans through clear ...
Guo Z +9 more
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Knowledge-Guided Symbolic Regression for Interpretable Camera Calibration [PDF]
Journal of ImagingCalibrating cameras accurately requires the identification of projection and distortion models that effectively account for lens-specific deviations. Conventional formulations, like the pinhole model or radial–tangential corrections, often struggle to ...
Rui Pimentel de Figueiredo
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Symbolic regression for strength prediction of eccentrically loaded concrete-filled steel tubular columns. [PDF]
Sci RepConcrete-filled steel tube (CFST) columns are widely employed in high-rise buildings, long-span bridges, and seismic-resistant structures due to their superior load-bearing capacity, structural efficiency, and resilience under extreme loading conditions.
Megahed K.
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AI Feynman: A physics-inspired method for symbolic regression. [PDF]
Sci Adv, 2020 Our physics-inspired algorithm for symbolic regression is able to discover complex physics equations from mere tables of numbers. A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression ...
Udrescu SM, Tegmark M.
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Predicting flexural strength of hybrid FRP-steel reinforced beams using symbolic regression and ML techniques. [PDF]
Sci RepHybrid fiber-reinforced polymer (FRP) and steel reinforced concrete (hybrid FRP-steel RC) beams have gained recognition for their exceptional flexural performance, surpassing that of beams reinforced exclusively with FRP bars (FRP-RC).
Megahed K.
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Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives. [PDF]
Arch Comput Methods Eng, 2023 Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data.
Angelis D, Sofos F, Karakasidis TE.
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Application of the symbolic regression program AI-Feynman to psychology [PDF]
Frontiers in Artificial Intelligence, 2023 The discovery of hidden laws in data is the core challenge in many fields, from the natural sciences to the social sciences. However, this task has historically relied on human intuition and experience in many areas, including psychology.
Masato Miyazaki +5 more
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Angular coefficients from interpretable machine learning with symbolic regression [PDF]
Journal of High Energy PhysicsWe explore the use of symbolic regression to derive compact analytical expressions for angular observables relevant to electroweak boson production at the Large Hadron Collider (LHC).
Josh Bendavid +4 more
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