Training spiking neural networks using lessons from deep learning
The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
Deep learning in computational mechanics: a review
L Herrmann, S Kollmannsberger - Computational Mechanics, 2024 - Springer
The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
A comparative study on different neural network architectures to model inelasticity
M Rosenkranz, KA Kalina, J Brummund… - … Journal for Numerical …, 2023 - Wiley Online Library
The mathematical formulation of constitutive models to describe the path‐dependent, that is,
inelastic, behavior of materials is a challenging task and has been a focus in mechanics …
inelastic, behavior of materials is a challenging task and has been a focus in mechanics …
Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics
SB Tandale, M Stoffel - Computer Methods in Applied Mechanics and …, 2023 - Elsevier
The present study aims to introduce an AI algorithm suitable for neuromorphic computing to
solve Boundary Value Problems in Engineering Mechanics. Following the trend of …
solve Boundary Value Problems in Engineering Mechanics. Following the trend of …
Neuroscience inspired neural operator for partial differential equations
S Garg, S Chakraborty - Journal of Computational Physics, 2024 - Elsevier
We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which
aims to bridge the gap between theoretical and practical implementation of Artificial …
aims to bridge the gap between theoretical and practical implementation of Artificial …
Spiking neural networks for detecting satellite internet-of-things signals
With the rapid growth of Internet of Things (IoT) networks, ubiquitous coverage is becoming
increasingly necessary. Low earth orbit (LEO) satellite constellations for the IoT have been …
increasingly necessary. Low earth orbit (LEO) satellite constellations for the IoT have been …
Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks
We applied physics-informed neural networks to solve the constitutive relations for
nonlinear, path-dependent material behavior. As a result, the trained network not only …
nonlinear, path-dependent material behavior. As a result, the trained network not only …
Spiking neural networks for detecting satellite-based internet-of-things signals
With the rapid growth of IoT networks, ubiquitous coverage is becoming increasingly
necessary. Low Earth Orbit (LEO) satellite constellations for IoT have been proposed to …
necessary. Low Earth Orbit (LEO) satellite constellations for IoT have been proposed to …
Data-driven spiking neural networks for intelligent fault detection in vehicle lithium-ion battery systems
P Wu, E Tian, H Tao, Y Chen - Engineering Applications of Artificial …, 2025 - Elsevier
Electric vehicles (EVs) powered by high-energy batteries are anticipated to be a primary
avenue for achieving energy decarbonization in future societies. However, the high energy …
avenue for achieving energy decarbonization in future societies. However, the high energy …
Openspike: An openram snn accelerator
F Modaresi, M Guthaus… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
This paper presents a spiking neural network (SNN) accelerator made using fully open-
source EDA tools, process design kit (PDK), and memory macros synthesized using Open …
source EDA tools, process design kit (PDK), and memory macros synthesized using Open …