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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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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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Glyph: Symbolic Regression Tools
Journal of Open Research Software, 2019 We present Glyph – a Python package for genetic programming based symbolic regression. Glyph is designed for usage in numerical simulations as well as real world experiments.
Markus Quade, Julien Gout, Markus Abel
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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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Improving eye-tracking calibration accuracy using symbolic regression. [PDF]
PLoS ONE, 2019 Eye tracking systems have recently experienced a diversity of novel calibration procedures, including smooth pursuit and vestibulo-ocular reflex based calibrations.
Almoctar Hassoumi +2 more
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Interactive symbolic regression with co-design mechanism through offline reinforcement learning [PDF]
Nature CommunicationsSymbolic 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 ...
Yuan Tian +5 more
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Revealing Complex Ecological Dynamics via Symbolic Regression. [PDF]
Bioessays, 2019 ABSTRACTComplex ecosystems, from food webs to our gut microbiota, are essential to human life. Understanding the dynamics of those ecosystems can help us better maintain or control them. Yet, reverse-engineering complex ecosystems (i.e., extracting their dynamic models) directly from measured temporal data has not been very successful so far.
Chen Y, Angulo MT, Liu YY.
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AI Feynman: A physics-inspired method for symbolic regression [PDF]
Science Advances, 2020 Silviu-Marian Udrescu, Max Erik Tegmark
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Extending a physics-based constitutive model using genetic programming
Applications in Engineering Science, 2022 In material science, models are derived to predict emergent material properties (e.g. elasticity, strength, conductivity) and their relations to processing conditions.
Gabriel Kronberger +3 more
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Genetic Programming and Evolvable Machines, 2021 Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages -- between 2--5 -- to a symbolic regressor, statistically significant improvements can often be attained ...
Moshe Sipper, Jason H. Moore
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