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ARXIV:2603.18328 · PHYSICS-INFORMED NEURAL NETWORKS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.18328PHYSICS-INFORMED NEURAL NETWORKSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEKrishna Murari · arXiv
Develop adaptive wavelet-based activation functions to improve the stability and accuracy of Physics-Informed Neural Networks for scientific and engineering problems.
Opportunity summary
Pain Develop adaptive wavelet-based activation functions to improve the stability and accuracy of Physics-Informed Neural Networks for scientific and engineering problems.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Develop adaptive wavelet-based activation functions to improve the stability and accuracy of Physics-Informed Neural Networks for scientific and engineering problems. It has demonstrated strong performance across a wide range of scientific and engineering problems.
Physics-Informed Neural Networks(PINNs) are a powerful and flexible learning framework that has gained significant attention in recent years. It has demonstrated strong performance across a wide range of scientific and engineering problems.
ScienceToStartup currently rates this 5.0/10 on the public viability pass. The proposed activation functions significantly improve training stability and expressive power by combining trainable wavelet functions with either trainable or fixed hyperbolic tangent and…
Physics-Informed Neural Networks moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
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Develop adaptive wavelet-based activation functions to improve the stability and accuracy of Physics-Informed Neural Networks for scientific and engineering problems.
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10.48550/arXiv.2603.18328Develop adaptive wavelet-based activation functions to improve the stability and accuracy of Physics-Informed Neural Networks for scientific and engineering problems.
Abstract
Physics-Informed Neural Networks(PINNs) are a powerful and flexible learning framework that has gained significant attention in recent years. It has demonstrated strong performance across a wide range of scientific and engineering problems. In parallel, wavelets have been extensively used as efficient computational tools due to their strong approximation capabilities. Motivated by the common failure modes observed in standard PINNs, this work introduces a novel family of adaptive wavelet-based activation functions. The proposed activation functions significantly improve training stability and expressive power by combining trainable wavelet functions with either trainable or fixed hyperbolic tangent and softplus functions. Five distinct activation functions are developed within the PINN framework and systematically evaluated across four representative classes of partial differential equations (PDEs). Comprehensive comparisons using bar plots demonstrate improved robustness and accuracy compared to traditional activation functions. Furthermore, the proposed approach is validated through direct comparisons with baseline PINNs, transformer-based architectures such as PINNsFormer, and other deep learning models, highlighting its effectiveness and generality.
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PROBLEM
Develop adaptive wavelet-based activation functions to improve the stability and accuracy of Physics-Informed Neural Networks for scientific and engineering problems. It has demonstrated strong performance across a wide range of scientific and engineering problems.
METHOD
Physics-Informed Neural Networks(PINNs) are a powerful and flexible learning framework that has gained significant attention in recent years. It has demonstrated strong performance across a wide range of scientific and engineering problems.
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. The proposed activation functions significantly improve training stability and expressive power by combining trainable wavelet functions with either trainable or fixed hyperbolic tangent and softplus func...
WHY NOW
Physics-Informed Neural Networks moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
Develop adaptive wavelet-based activation functions to improve the stability and accuracy of Physics-Informed Neural Networks for scientific and engineering problems. It has demonstrated strong performance across a wide range of scientific and engineering problems.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Physics-Informed Neural Networks(PINNs) are a powerful and flexible learning framework that has gained significant attention in recent years. It has demonstrated strong performance across a wide range of scientific and engineering problems.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. The proposed activation functions significantly improve training stability and expressive power by combining trainable wavelet functions with either trainable or fixed hyperbolic tangent and softplus functions. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Physics-Informed Neural Networks moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Develop adaptive wavelet-based activation functions to improve the stability and accuracy of Physics-Informed Neural Networks for scientific and engineering problems.
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Physics-Informed Neural Networks
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