arXiv:2605.24278v1 Announce Type: new Abstract: We present an improved neural field architecture for solving partial differential equations (PDEs). Current physics-informed neural networks (PINNs) provide a flexible framework for solving PDEs, but they struggle to achieve highly accurate solutions and require computation that scales poorly with parameter count. Our model, which we call beignet (Bandlimited Embedding with Interpolated Grid Network), replaces the random Fourier feature embedding used by existing PINN models with a trainable multi-resolution Fourier feature pyramid. To query beig
The paper addresses a core limitation of current Physics-Informed Neural Networks (PINNs) – their computational cost and accuracy issues – just as their application space is expanding.
Improving the accuracy and efficiency of PINNs could significantly accelerate scientific discovery, engineering design, and the development of more robust AI systems for complex physical phenomena.
The 'beignet' architecture offers a potentially more scalable and accurate method for solving partial differential equations (PDEs), which underlie many scientific and engineering problems.
- · AI researchers and developers
- · Engineering simulation and design firms
- · Scientific research institutions
- · Sectors reliant on complex simulations (e.g., aerospace, climate modeling)
- · Traditional numerical PDE solvers that are less adaptable
- · Cloud computing providers if models become significantly more efficient
More accurate and efficient AI models for simulating physical systems become accessible.
This could lead to a faster design cycle for new materials, drugs, and industrial processes.
Accelerated scientific breakthroughs powered by AI-driven simulations reshape entire research fields.
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Read at arXiv cs.LG