7 citations · 7 across the 4 of their papers we have counts for
5 papers
A Decomposable Forward Process in Diffusion Models for Time-Series Forecasting
Francisco Caldas, Sahil Kumar, Cláudia Soares
We introduce a model-agnostic forward diffusion process for time-series forecasting that decomposes signals into spectral components, preserving structured temporal patterns such a…
Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach
Katarina Dyreby, Francisco Caldas, Cláudia Soares
In the era of mega-constellations, the need for accurate and publicly available information has become fundamental for satellite operators to guarantee the safety of spacecrafts an…
OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning
Alexandre Oliveira, Katarina Dyreby, Francisco Caldas +1
The increasing number of satellites and orbital debris has made space congestion a critical issue, threatening satellite safety and sustainability. Challenges such as collision avo…
Advancing Solutions for the Three-Body Problem Through Physics-Informed Neural Networks
Manuel Santos Pereira, Luís Tripa, Nélson Lima +2
First formulated by Sir Isaac Newton in his work "Philosophiae Naturalis Principia Mathematica", the concept of the Three-Body Problem was put forth as a study of the motion of the…
Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables
Francisco Caldas, Cláudia Soares
The increasing volume of space objects in Earth's orbit presents a significant challenge for Space Situational Awareness (SSA). And in particular, accurate orbit prediction is cruc…