56 citations · 66 across the 6 of their papers we have counts for
6 papers
A Mutual Information Lower Bound for Multimodal Regression Active Learning
Leonardo Ferreira Guilhoto, Akshat Kaushal, Paris Perdikaris
Active learning for continuous regression has lacked an acquisition function that targets epistemic uncertainty when the predictive distribution is multimodal: variance misses moda…
Multimodal Scientific Learning Beyond Diffusions and Flows
Leonardo Ferreira Guilhoto, Akshat Kaushal, Paris Perdikaris
Scientific machine learning (SciML) increasingly requires models that capture multimodal conditional uncertainty arising from ill-posed inverse problems, multistability, and chaoti…
Active Learning Design: Modeling Force Output for Axisymmetric Soft Pneumatic Actuators
Gregory M. Campbell, Gentian Muhaxheri, Leonardo Ferreira Guilhoto +4
Soft pneumatic actuators (SPA) made from elastomeric materials can provide large strain and large force. The behavior of locally strain-restricted hyperelastic materials under infl…
Deep Learning Alternatives of the Kolmogorov Superposition Theorem
Leonardo Ferreira Guilhoto, Paris Perdikaris
This paper explores alternative formulations of the Kolmogorov Superposition Theorem (KST) as a foundation for neural network design. The original KST formulation, while mathematic…
Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks
Leonardo Ferreira Guilhoto, Paris Perdikaris
Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper,…
Learning Operators with Coupled Attention
Georgios Kissas, Jacob Seidman, Leonardo Ferreira Guilhoto +3
Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution of spatio-temporal dynamical systems and approximating general bla…