Sinusoidal Representation Networks

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Complementary, Not Cumulative: Interaction Effects in Physics-Informed Neural Networks for Navier-Stokes Vortex Shedding
Aug 20, 2026Devesh ShahSinusoidal Representation Networks Physics-Informed ML
Independent Researcher
Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation
Jul 23, 2026Hyunmin Cho, Jaejun Yoo, Kyong Hwan JinImplicit Neural Representations Sinusoidal Representation Networks
Department of Electrical Engineering, Korea University, Seoul, South Korea · Graduate School of Artificial Intelligence, UNIST, Ulsan, South Korea
FiRe: Frequency Reparameterization as a Preconditioner for Periodic Implicit Neural Representations
Jun 28, 2026Harinandan Shukla, Rajarshi Verma, Jitin SinglaImplicit Neural Representations Neural Network Optimization
Department of Computer Science and Engineering, IIT Roorkee · Department of Biosciences and Bioengineering, IIT Roorkee
SirenFNO: Efficient and Full Frequency Learning of Fourier Neural Operators
Jun 9, 2026Pengqing Shi, Jie Yin, Stephen Tierney +1Fourier Neural Operator Spectral Bias
The University of Sydney, Australia
Continuous Neural Reparameterization as a Deep Geometric Prior for Robust Fixed-Chart UV Repair
Jun 8, 2026Mohammad Sadegh SalehiSinusoidal Representation Networks
Zero One Creative London, UK
Architecture Shapes Transfer Specificity in Implicit Neural Representations
Jun 5, 2026D Yang EngNeural PDE Solvers Implicit Neural Representations
Jacobi-Anger Method for Deterministic Initialization in Implicit Neural Representation
Jun 4, 2026Mohammed Alsakabi, Kejia Hu, John M. Dolan +1Implicit Neural Representations Neural Network Initialization
Department of Electrical and Computer Engineering, College of Engineering · The Robotics Institute, School of Computer Science Carnegie Mellon University, Pittsburgh, PA, USA
Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data
May 29, 2026Haoan Feng, Xin Xu, Leila De FlorianiImplicit Neural Representations Geospatial Representation Learning
University of Maryland, College Park MD, USA
Learning to Rotate: Temporal and Semantic Rotary Encoding for Sequential Modeling
Apr 27, 2026Hailing Cheng, Daqi Sun, Xinyu LuRotary Positional Embeddings Self-Attention
LinkedIn Inc. Mountain View, CA 94043
High-Fidelity Compression of Seismic Velocity Models via SIREN Auto-Decoders
Mar 15, 2026Caiyun Liu, Xiaoxue Luo, Jie XiongSinusoidal Representation Networks Model Compression
School of Information and Mathematics, Yangtze University, Jingzhou, Hubei 434023, P.R. China · School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, Hubei 434023, P.R. China

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Complementary, Not Cumulative: Interaction Effects in Physics-Informed Neural Networks for Navier-Stokes Vortex Shedding

Devesh Shah

Independent Researcher

Sinusoidal Representation Networks Physics-Informed ML Neural Network Training Dynamics Computational Fluid Dynamics

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Physics-informed neural networks (PINNs) embed governing partial differential equations directly into the training loss, offering a promising alternative to costly CFD solvers for unsteady flows. Yet the growing list of techniques proposed to improve PINN training is typically validated one at a time, leaving open whether these techniques actually compose. We study this question in depth on the DFG/Schafer-Turek unsteady cylinder wake benchmark. In isolation, nearly every technique performs no better than an untreated baseline. However, combining periodic (SIREN) activations with causal weighting unlocks a previously inaccessible regime, reconstructing velocity and pressure fields to within 4.1% average relative L2 error against an OpenFOAM reference solution. Adding further techniques instead causes catastrophic performance degradation, demonstrating that individually effective PINN interventions can interact nonlinearly and that more elaborate training recipes are not necessarily better.