Results 111 to 120 of about 2,854,398 (286)

Discovering equations from data: symbolic regression in dynamical systems

Journal of Physics: Complexity
The process of discovering equations from data lies at the heart of physics and in many other areas of research, including mathematical ecology and epidemiology.
Beatriz R Brum, Luiza Lober, Isolde Previdelli, Francisco A Rodrigues   +3 more
doaj   +1 more source

LLM‐Integrated Human–Robot Interaction System for Microrobots

Advanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source

Orbital anomaly reconstruction using deep symbolic regression [PDF]

, 2020
This work explores the combination of Sparse and Symbolic Regression, here called Deep Symbolic Regression, for the autonomous reconstruction of orbital anomalies.
Vasile, Massimiliano, Manzi, Matteo
core   +1 more source

Force–Displacement Characterization of PNIPAM Microactuators

Advanced Robotics Research, EarlyView.
A method to experimentally establish a force–displacement diagram of poly(N‐isopropyl acrylamide) (PNIPAM) microactuators, printed by 2PP, is presented herein. PNIPAM is a hydrogel whose volume and stiffness change with temperature due to the absorption or release of water.
Manon Cassignol, Esra Yalcinkaya, Adam Chafaï, Jérémy Odent, Pierre Lambert   +4 more
wiley   +1 more source

Inferring interpretable models of fragmentation functions using symbolic regression

Machine Learning: Science and Technology
Machine learning is rapidly making its path into the natural sciences, including high-energy physics. We present the first study that infers, directly from experimental data, a functional form of fragmentation functions.
Nour Makke, Sanjay Chawla
doaj   +1 more source

Enhancing Amphibious Flipper‐Based Locomotion Through Terrain‐Dependent Bayesian Optimization of Central Pattern Generator Control

Advanced Robotics Research, EarlyView.
A sea turtle‐inspired amphibious robot uses simulation‐guided optimization to adapt its flipper coordination to different environments. Sensitivity analysis and multiobjective Bayesian optimization identify terrain‐dependent controllers that are validated on hardware.
Nnamdi C. Chikere, Yasemin Ozkan‐Aydin
wiley   +1 more source

Linear regression analysis predicting symbolic scores at Time 2 with symbolic and nonsymbolic scores at Time 1 as predictors.

, 2016
Linear regression analysis predicting symbolic scores at Time 2 with symbolic and nonsymbolic scores at Time 1 as predictors.
Daniel Ansari (429649), Anna A. Matejko (1843375)   +1 more
core   +1 more source

Angular coefficients from interpretable machine learning with symbolic regression

Journal of High Energy Physics
We 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, Daniel Conde, Manuel Morales-Alvarado, Veronica Sanz, Maria Ubiali   +4 more
doaj   +1 more source

XeraBorg: Neural Control of Tail‐Flick Escape for Backward Propulsion in Cyborg Crayfish

Advanced Robotics Research, EarlyView.
This work presents the first controlled tail‐flick propulsion method for the cyborg Cherax quadricarinatus. Comprehensive experiments establish a foundational framework for using the crayfish's natural escape mechanism as a locomotion strategy, expanding its potential as an underwater and amphibious mobile robot.
H. Nhan Le, Oscar J. Davis, Lachlan Fitzgerald, Robbie Wilson, Christofer Clemente, P. Ross McAree, T. Nho Do, T. Thang Vo‐Doan   +7 more
wiley   +1 more source

Deep Learning for Symbolic Regression

, 2022
Symbolic regression is a task of finding mathematical equation based on the observed data. Historically, genetic programming was the main tool to tackle the symbolic regres- sion, however, recently, new neural network based approaches emerged.
Vastl, Martin
core