Search Results for author:
Found 9 papers, 3 papers with code
Engineering software 2.0 by interpolating neural networks: unifying training, solving, and calibration
The evolution of artificial intelligence (AI) and neural network theories has revolutionized the way software is programmed, shifting from a hard-coded series of codes to a vast neural network.
Gradient-free neural topology optimization
Gradient-free optimizers allow for tackling problems regardless of the smoothness or differentiability of their objective function, but they require many more iterations to converge when compared to gradient-based algorithms.
Continual learning for surface defect segmentation by subnetwork creation and selection
We introduce a new continual (or lifelong) learning algorithm called LDA-CP&S that performs segmentation tasks without undergoing catastrophic forgetting.
iPINNs: Incremental learning for Physics-informed neural networks
Physics-informed neural networks (PINNs) have recently become a powerful tool for solving partial differential equations (PDEs).
Cooperative data-driven modeling
However, artificial neural networks suffer from catastrophic forgetting, i. e. they forget how to perform an old task when trained on a new one.
Continual Prune-and-Select: Class-incremental learning with specialized subnetworks
In particular, CP&S is capable of sequentially learning 10 tasks from ImageNet-1000 keeping an accuracy around 94% with negligible forgetting, a first-of-its-kind result in class-incremental learning.
Adaptivity for clustering-based reduced-order modeling of localized history-dependent phenomena
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-order models (CROMs).
Neural network relief: a pruning algorithm based on neural activity
Current deep neural networks (DNNs) are overparameterized and use most of their neuronal connections during inference for each task.
Spiderweb nanomechanical resonators via Bayesian optimization: inspired by nature and guided by machine learning
From ultra-sensitive detectors of fundamental forces to quantum networks and sensors, mechanical resonators are enabling next-generation technologies to operate in room temperature environments.
