Tootfinder

Vertex addition to a ball graph with application to reliability and area coverage in autonomous swarms
Calum Buchanan, Puck Rombach, James Bagrow, Hamid R. Ossareh
https://arxiv.org/abs/2506.19197

Vertex addition to a ball graph with application to reliability and area coverage in autonomous swarms
A unit ball graph consists of a set of vertices, labeled by points in Euclidean space, and edges joining all pairs of points within distance $1$. These geometric graphs are used to model a variety of spatial networks, including communication networks between agents in an autonomous swarm. In such an application, vertices and/or edges of the graph may not be perfectly reliable; an agent may experience failure or a communication link rendered inoperable. With the goal of designing robust swarm fo…

Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
Ashfaq Iftakher, Rahul Golder, M. M. Faruque Hasan
https://arxiv.org/abs/2507.08124 https://arxiv.org/pdf/2507.08124 https://arxiv.org/html/2507.08124
arXiv:2507.08124v1 Announce Type: new
Abstract: Traditional physics-informed neural networks (PINNs) do not guarantee strict constraint satisfaction. This is problematic in engineering systems where minor violations of governing laws can significantly degrade the reliability and consistency of model predictions. In this work, we develop KKT-Hardnet, a PINN architecture that enforces both linear and nonlinear equality and inequality constraints up to machine precision. It leverages a projection onto the feasible region through solving Karush-Kuhn-Tucker (KKT) conditions of a distance minimization problem. Furthermore, we reformulate the nonlinear KKT conditions using log-exponential transformation to construct a general sparse system with only linear and exponential terms, thereby making the projection differentiable. We apply KKT-Hardnet on both test problems and a real-world chemical process simulation. Compared to multilayer perceptrons and PINNs, KKT-Hardnet achieves higher accuracy and strict constraint satisfaction. This approach allows the integration of domain knowledge into machine learning towards reliable hybrid modeling of complex systems.
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Low noise flux estimate and data quality control monitoring in EUCLID-NISP cosmological survey
B. Kubik, R. Barbier, P. Calabria, A. Castera, E. Chabanat, F. Charlieu, J-C. Clemens, A. Ealet, S. Ferriol, W. Gillard, T. Maciaszek, E. Prieto, F. Schirra, A. Secroun, B. Serra, G. Smadja, A. Tilquin, J. Zoubianb
https://arxiv.org/ab…

Low noise flux estimate and data quality control monitoring in EUCLID-NISP cosmological survey
Euclid mission is designed to understand the dark sector of the universe. Precise redshift measurements are provided by H2RG detectors. We propose an unbiased method of fitting the flux with Poisson distributed and correlated data, which has an analytic solution and provides a reliable quality factor - fundamental features to ensure the goals of the mission. We compare our method to other techniques of signal estimation and illustrate the anomaly detection on the flight like detectors. Although…