Results 71 to 80 of about 2,854,398 (286)

An Integrated Oxygen‐Supplying Device and Predictive Multiphysics Model for Extended Ambient Transport of Insulin‐Producing Cells

Advanced Functional Materials, EarlyView.
Cell therapies typically rely on cold‐chain logistics and cryopreservation, limiting access and compromising cell quality. Here, a dual‐chamber device separates an oxygen‐supplying chamber from a hyaluronic acid cargo chamber, sustaining oxygen delivery for over 70 h and enabling ambient‐temperature shipment.
Daniel A. Domingo‐Lopez, Ruth Tarpey, Benjamin Brennan, Bryan D. Boyle, Amir M. Alsharabasy, Abhik Mallick, Gabriella Bellavia, Abhay Pandit, Garry P. Duffy, Ruth E. Levey   +9 more
wiley   +1 more source

Accelerating graph-based tracking tasks with symbolic regression

Machine Learning: Science and Technology
The reconstruction of particle tracks from hits in tracking detectors is a computationally intensive task due to the large combinatorics of detector signals.
Nathalie Soybelman, Carlo Schiavi, Francesco A Di Bello, Eilam Gross   +3 more
doaj   +1 more source

Symbolic Regression for State Estimation of Lithium-Ion Battery

IEEE Access
Modeling lithium-ion batteries has been a challenging problem. One of the critical tasks among many is state estimation, as it enables researchers to design better battery management systems (BMS).
Anubhav Kamal, Shubham Sambhaji Patil, Sagar Bharathraj, Ankur Deshwal, Shashishekar P. Adiga   +4 more
doaj   +1 more source

Symbolic regression metamodel based multi-response optimization of EDM process [PDF]

FME Transactions, 2020
Electrical Discharge Machining (EDM) is a popular non-traditional machining process that is widely used due to its ability to machine hard and brittle materials. It does not require a cutting tool and can machine complex geometries easily.
Ghadai Ranjan Kumar, Kalita Kanak, Gao Xiao-Zhi   +2 more
doaj  

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source

Symbolic Modeling for financial asset pricing

Journal of Finance and Data Science
Symbolic Regression is a machine learning technique that discovers an unknown function from its samples. Compared to conventional regression techniques (e.g., linear regression, polynomial regression, etc.), Symbolic Regression does not limit the ...
Xiangwu Zuo, Anxiao (Andrew) Jiang
doaj   +1 more source

Nitride MXenes Beyond Carbides: Bridging the Gap Between Computational Prediction and Experimental Realization

Advanced Functional Materials, EarlyView.
Nitride MXenes remain constrained by a persistent gap between computational prediction and experimental realization. This Review identifies the thermodynamic, kinetic, and chemical barriers limiting their synthesis, critically evaluates emerging fabrication routes, and proposes a multidimensional computational‐experimental framework to accelerate the ...
Naresh Varnakavi, Masoud Soroush
wiley   +1 more source

Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing [PDF]

The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression ...
Teeuwen, G.J.A., Rennen, G., Stinstra, E.   +2 more
core  

Robust regression with imprecise data [PDF]

, 2011
We consider the problem of regression analysis with imprecise data. By imprecise data we mean imprecise observations of precise quantities in the form of sets of values.
Wiencierz, Andrea, Cattaneo, Marco E. G. V.   +1 more
core   +1 more source

A New Approximation for the Perimeter of an Ellipse

Algorithms
We consider the problem of approximating the perimeter of an ellipse, for which there is no known finite formula, in the context of high-precision performance.
Pablo Moscato, Andrew Ciezak
doaj   +1 more source