12 papers
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models
Conor Rowan
Scientists have historically relied on mathematical models based on differential equations to relate system inputs -- forces, fluxes, or heat sources -- to outputs, such as displac…
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…
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.…
Visualizing the loss landscapes of physics-informed neural networks
Conor Rowan, Finn Murphy-Blanchard
Training a neural network requires navigating a high-dimensional, non-convex loss surface to find parameters that minimize this loss. In many ways, it is surprising that optimizers…
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…
On the failure of ReLU activation for physics-informed machine learning
Conor Rowan
Physics-informed machine learning uses governing ordinary and/or partial differential equations to train neural networks to represent the solution field. Like any machine learning…