ArticleProceedings. Mathematical, physical, and engineering sciences2020
Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks.
Article in Proceedings. Mathematical, physical, and engineering sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- A compact and efficient physics-informed architecture for reconstructing and predicting complex physical fields from low accuracy sparse data.Research square · 2026Article
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- Barrier Lyapunov function-based robust adaptive neural network control with dynamically adjusted activation functions for Euler-Lagrange systems under position constraints.Scientific reports · 2026Article
- The finite element neural network method to simulate two dimensional partial differential equations and perform parameter identification.Scientific reports · 2026Article
- Article
- NeuroPlast: a learnable activation function evaluated under knowledge distillation for medical image classification.Frontiers in artificial intelligence · 2026Article
- A variational framework for residual-based adaptivity in neural PDE solvers and operator learning.NPJ artificial intelligence · 2026Article
- Fast electromagnetic field simulation using a current-density- based physics-informed neural network.Scientific reports · 2025Article
- Inverse solution of process parameters in gear grinding using hierarchical bayesian physics informed neural network (HBPINN).Scientific reports · 2025Article
- Unifying machine learning and interpolation theory via interpolating neural networks.Nature communications · 2025Article
- From PINNs to PIKANs: recent advances in physics-informed machine learning.Machine learning for computational science and engineering · 2025Article
- AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks.Science advances · 2025Article
- A deep learning approach: physics-informed neural networks for solving a nonlinear telegraph equation with different boundary conditions.BMC research notes · 2025Article
- Homotopy Relaxation Training Algorithms for Infinite-Width Two-Layer ReLU Neural Networks.Journal of scientific computing · 2025Article
- Article
- Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks.Neural networks : the official journal of the International Neural Network Society · 2024Article
- Physics-Informed Machine Learning for Accurate Prediction of Temperature and Melt Pool Dimension in Metal Additive Manufacturing.3D printing and additive manufacturing · 2024Article
- Efficient Neural Networks on the Edge with FPGAs by Optimizing an Adaptive Activation Function.Sensors (Basel, Switzerland) · 2024Article
- Deep Learning Techniques to Characterize theBiomedicines · 2024Article
- Accelerated simulation methodologies for computational vascular flow modelling.Journal of the Royal Society, Interface · 2024Review
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3 authors.
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Abstract
We propose two approaches of locally adaptive activation functions namely, layer-wise and neuron-wise locally adaptive activation functions, which improve the performance of deep and physics-informed neural networks. The local adaptation of activation function is achieved by introducing a scalable parameter in each layer (layer-wise) and for every neuron (neuron-wise) separately, and then optimizing it using a variant of stochastic gradient descent algorithm. In order to further increase the training speed, an activation slope-based
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