3 citations · 6 across the 7 of their papers we have counts for
8 papers
Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization
Grant Norman, Conor Rowan, Kurt Maute +1
In this work, we investigate the use of data-driven equation discovery for dynamical systems to model and forecast continuous-time dynamics of unconstrained optimization problems.…
Boundary condition enforcement with PINNs: a comparative study and verification on 3D geometries
Conor Rowan, Kai Hampleman, Kurt Maute +1
Since their advent nearly a decade ago, physics-informed neural networks (PINNs) have been studied extensively as a novel technique for solving forward and inverse problems in phys…
Calibrating a Finite-strain Phase-field Model of Fracture for Bonded Granular Materials with Uncertainty Quantification
Abigail C. Schmid, Erik Jensen, Fabio Di Gioacchino +8
To study the mechanical behavior of mock high explosives, an experimental and simulation program was developed to calibrate, with quantified uncertainty, a material model of the bo…
Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
Conor Rowan, John Evans, Kurt Maute +1
From characterizing the speed of a thermal system's response to computing natural modes of vibration, eigenvalue analysis is ubiquitous in engineering. In spite of this, eigenvalue…
Physics-informed solution reconstruction in elasticity and heat transfer using the explicit constraint force method
Conor Rowan, Kurt Maute, Alireza Doostan
One use case of ``physics-informed neural networks'' (PINNs) is solution reconstruction, which aims to estimate the full-field state of a physical system from sparse measurements.…
On the definition and importance of interpretability in scientific machine learning
Conor Rowan, Alireza Doostan
Though neural networks trained on large datasets have been successfully used to describe and predict many physical phenomena, there is a sense among scientists that, unlike traditi…
Ensemble WSINDy for Data Driven Discovery of Governing Equations from Laser-based Full-field Measurements
Abigail C. Schmid, Alireza Doostan, Fatemeh Pourahmadian
This work leverages laser vibrometry and the weak form of the sparse identification of nonlinear dynamics (WSINDy) for partial differential equations to learn macroscale governing…
Constrained or Unconstrained? Neural-Network-Based Equation Discovery from Data
Grant Norman, Jacqueline Wentz, Hemanth Kolla +2
Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or accurat…