NeuralMOVES: Extracting and Learning Surrogates for Diverse Vehicle Emission Models
Technological advancements and interventions in the transportation sector play a crucial role in addressing climate change, given its major contribution to greenhouse gas emissions. The industry actively explores electrification, automation, and Intelligent Infrastructure to mitigate emissions. Howe...
Main Author: | Ramirez Sanchez, Edgar |
---|---|
Other Authors: | Wu, Cathy |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2024
|
Online Access: | https://hdl.handle.net/1721.1/157029 |
Similar Items
-
Surrogate modeling of vehicle dynamics using Recurrent Neural Networks
by: Kohei MAKINO, et al.
Published: (2020-09-01) -
Neural network surrogate models for absorptivity and emissivity spectra of multiple elements
by: Michael D. Vander Wal, et al.
Published: (2022-06-01) -
Toward the Usage of Deep Learning Surrogate Models in Ground Vehicle Aerodynamics
by: Benet Eiximeno, et al.
Published: (2024-03-01) -
MULTI-OBJECTIVE OPTIMIZATION OF VEHICLE/TRACK PARAMETERS BASED ON RBF NEURAL NETWORK SURROGATE MODEL
by: XIAO Qian, et al.
Published: (2021-01-01) -
Research on Vehicle Emission Factors in Wuhan Based on MOVES
by: Wang Xinyue, et al.
Published: (2024-01-01)