Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Anthony LoRe Starleaf, Senior
Lead Presenter's Name
Anthony LoRe Starleaf
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Siddharth Parida
Abstract
This study builds upon a previous investigation of Finite-Element Physics-Informed Neural Networks (FE-PINNs) by performing an analysis of their sensitivity to noise. FE-PINNs were previously shown to be capable of performing a two-dimensional linear elastic full waveform inversion on a soil column. As a further step towards applying this methodology to problems involving real data, FE-PINNs were used to inversely determine the elastic modulus of a single quad element, with varying degrees of noise (0-20%) present in the training data. It was found that, depending on the accuracy of the initial estimate of the element's elastic modulus, FE-PINN can successfully solve the inverse problem with up to 20% noise in the training data.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Spark Grant
Solving Inverse Problems Using Finite-Element Physics Informed Neural Networks in Presence of Noise
This study builds upon a previous investigation of Finite-Element Physics-Informed Neural Networks (FE-PINNs) by performing an analysis of their sensitivity to noise. FE-PINNs were previously shown to be capable of performing a two-dimensional linear elastic full waveform inversion on a soil column. As a further step towards applying this methodology to problems involving real data, FE-PINNs were used to inversely determine the elastic modulus of a single quad element, with varying degrees of noise (0-20%) present in the training data. It was found that, depending on the accuracy of the initial estimate of the element's elastic modulus, FE-PINN can successfully solve the inverse problem with up to 20% noise in the training data.