PINGS: Physics-Informed Neural Network for Fast Generative Sampling
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914036334985216 |
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| author | Prasha, Achmad Ardani Rachmadi, Clavino Ourizqi Syahlan, Muhamad Fauzan Ibnu Anugerah, Naufal Rahfi Raditya, Nanda Garin Amelia, Putri Mutiara, Sabrina Laila Ramadhan, Hilman Syachr |
| author_facet | Prasha, Achmad Ardani Rachmadi, Clavino Ourizqi Syahlan, Muhamad Fauzan Ibnu Anugerah, Naufal Rahfi Raditya, Nanda Garin Amelia, Putri Mutiara, Sabrina Laila Ramadhan, Hilman Syachr |
| contents | We introduce PINGS (Physics-Informed Neural Network for Fast Generative Sampling), a framework that amortizes diffusion sampling by training a physics-informed network to approximate reverse-time probability-flow dynamics, reducing sampling to a single forward pass (NFE = 1). As a proof of concept, we learn a direct map from a 3D standard normal to a non-Gaussian Gaussian Mixture Model (GMM). PINGS preserves the target's distributional structure (multi-bandwidth kernel $MMD^2 = 1.88 \times 10^{-2}$ with small errors in mean, covariance, skewness, and excess kurtosis) and achieves constant-time generation: $10^4$ samples in $16.54 \pm 0.56$ millisecond on an RTX 3090, versus 468-843 millisecond for DPM-Solver (10/20) and 960 millisecond for DDIM (50) under matched conditions. We also sanity-check the PINN/automatic-differentiation pipeline on a damped harmonic oscillator, obtaining MSEs down to $\mathcal{O}(10^{-5})$. Compared to fast but iterative ODE solvers and direct-map families (Flow, Rectified-Flow, Consistency), PINGS frames generative sampling as a PINN-style residual problem with endpoint anchoring, yielding a white-box, differentiable map with NFE = 1. These proof-of-concept results position PINGS as a promising route to fast, function-based generative sampling with potential extensions to scientific simulation (e.g., fast calorimetry). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_11284 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PINGS: Physics-Informed Neural Network for Fast Generative Sampling Prasha, Achmad Ardani Rachmadi, Clavino Ourizqi Syahlan, Muhamad Fauzan Ibnu Anugerah, Naufal Rahfi Raditya, Nanda Garin Amelia, Putri Mutiara, Sabrina Laila Ramadhan, Hilman Syachr Machine Learning Computational Physics We introduce PINGS (Physics-Informed Neural Network for Fast Generative Sampling), a framework that amortizes diffusion sampling by training a physics-informed network to approximate reverse-time probability-flow dynamics, reducing sampling to a single forward pass (NFE = 1). As a proof of concept, we learn a direct map from a 3D standard normal to a non-Gaussian Gaussian Mixture Model (GMM). PINGS preserves the target's distributional structure (multi-bandwidth kernel $MMD^2 = 1.88 \times 10^{-2}$ with small errors in mean, covariance, skewness, and excess kurtosis) and achieves constant-time generation: $10^4$ samples in $16.54 \pm 0.56$ millisecond on an RTX 3090, versus 468-843 millisecond for DPM-Solver (10/20) and 960 millisecond for DDIM (50) under matched conditions. We also sanity-check the PINN/automatic-differentiation pipeline on a damped harmonic oscillator, obtaining MSEs down to $\mathcal{O}(10^{-5})$. Compared to fast but iterative ODE solvers and direct-map families (Flow, Rectified-Flow, Consistency), PINGS frames generative sampling as a PINN-style residual problem with endpoint anchoring, yielding a white-box, differentiable map with NFE = 1. These proof-of-concept results position PINGS as a promising route to fast, function-based generative sampling with potential extensions to scientific simulation (e.g., fast calorimetry). |
| title | PINGS: Physics-Informed Neural Network for Fast Generative Sampling |
| topic | Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2509.11284 |