83 citations · 230 across the 9 of their papers we have counts for
9 papers
Interpolating neural network: A novel unification of machine learning and interpolation theory
Chanwook Park, Sourav Saha, Jiachen Guo +8
Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1.0) to neural network-based approaches (Software 2.0). However, a…
Centimeter-scale nanomechanical resonators with low dissipation
Andrea Cupertino, Dongil Shin, Leo Guo +3
High-aspect-ratio mechanical resonators are pivotal in precision sensing, from macroscopic gravitational wave detectors to nanoscale acoustics. However, fabrication challenges and…
High-Strength Amorphous Silicon Carbide for Nanomechanics
Minxing Xu, Dongil Shin, Paolo M. Sberna +4
For decades, mechanical resonators with high sensitivity have been realized using thin-film materials under high tensile loads. Although there have been remarkable strides in achie…
iPINNs: Incremental learning for Physics-informed neural networks
Aleksandr Dekhovich, Marcel H. F. Sluiter, David M. J. Tax +1
Physics-informed neural networks (PINNs) have recently become a powerful tool for solving partial differential equations (PDEs). However, finding a set of neural network parameters…
Cooperative data-driven modeling
Aleksandr Dekhovich, O. Taylan Turan, Jiaxiang Yi +1
Data-driven modeling in mechanics is evolving rapidly based on recent machine learning advances, especially on artificial neural networks. As the field matures, new data and models…
Continual Prune-and-Select: Class-incremental learning with specialized subnetworks
Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter +1
The human brain is capable of learning tasks sequentially mostly without forgetting. However, deep neural networks (DNNs) suffer from catastrophic forgetting when learning one task…
Adaptivity for clustering-based reduced-order modeling of localized history-dependent phenomena
Bernardo P. Ferreira, F. M. Andrade Pires, Miguel A. Bessa
This paper proposes a novel Adaptive Clustering-based Reduced-Order Modeling (ACROM) framework to significantly improve and extend the recent family of clustering-based reduced-ord…
Neural network relief: a pruning algorithm based on neural activity
Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter +1
Current deep neural networks (DNNs) are overparameterized and use most of their neuronal connections during inference for each task. The human brain, however, developed specialized…
Spiderweb nanomechanical resonators via Bayesian optimization: inspired by nature and guided by machine learning
Dongil Shin, Andrea Cupertino, Matthijs H. J. de Jong +3
From ultra-sensitive detectors of fundamental forces to quantum networks and sensors, mechanical resonators are enabling next-generation technologies to operate in room temperature…