4 papers
RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
Ramiro Valdes Jara, David Chapman, Adam Meyers
Multivariate time series imputation (MTSI) aims to recover missing values in temporal data composed of multiple interdependent variables. This problem is central to real-world appl…
SynthCharge: An Electric Vehicle Routing Instance Generator with Feasibility Screening to Enable Learning-Based Optimization and Benchmarking
Mertcan Daysalilar, Fuat Uyguroglu, Gabriel Nicolosi +1
The electric vehicle routing problem with time windows (EVRPTW) extends the classical VRPTW by introducing battery capacity constraints and charging station decisions. Existing ben…
A Curriculum-Based Deep Reinforcement Learning Framework for the Electric Vehicle Routing Problem
Mertcan Daysalilar, Fuat Uyguroglu, Gabriel Nicolosi +1
The electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel dista…
Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning
Mominul Rubel, Adam Meyers, Gabriel Nicolosi
We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedf…