5 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…
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.…
Enriched Immersed Finite Element and Isogeometric Analysis -- Algorithms and Data Structures
Nils Wunsch, Keenan Doble, Mathias R. Schmidt +3
Immersed finite element methods provide a convenient analysis framework for problems involving geometrically complex domains, such as those found in topology optimization and micro…