mlsanityData to 7 Oct 2026

Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling

arXiv 2402.18846 ↗First posted 29 Feb 2024cs.LGcited by 13

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Abstract (arXiv)

Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources. Traditional methods relying on Gaussian processes can hardly scale to high-dimensional data. Deep learning approaches utilize neural network based encoders and decoders to improve scalability. These approaches share encoded representations across fidelities without including corresponding decoder parameters. This hinders inference performance, especially in out-of-distribution scenarios when the highest fidelity data has limited domain coverage. To address these limitations, we propose Multi-fidelity Residual Neural Processes (MFRNP), a novel multi-fidelity surrogate modeling framework. MFRNP explicitly models the residual between the aggregated output from lower fidelities and ground truth at the highest fidelity. The aggregation introduces decoders into the information sharing step and optimizes lower fidelity decoders to accurately capture both in-fidelity and cross-fidelity information. We show that MFRNP significantly outperforms state-of-the-art in learning partial differential equations and a real-world climate modeling task. Our code is published at: https://github.com/Rose-STL-Lab/MFRNP

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 29 of its 75 references that are in the map. First posted on arXiv 29 Feb 2024.

Builds on

29 of 75 references.

References · 29

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17 more
  1. 2019Deep Gaussian Processes for Multi-fidelity Modelingcited by 48
  2. 2020Multi-Fidelity Bayesian Optimization via Deep Neural Networkscited by 46
  3. 2019The Functional Neural Processcited by 43
  4. 2020Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variablescited by 33
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  14. 2023Disentangled Multi-Fidelity Deep Bayesian Active Learningcited by 11
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Citing papers per year: 2024: 2; 2025: 6; 2026: 5.

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  1. 2024Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptationcited by 7
  2. 2025Spatiotemporal Pyramid Flow Matching for Climate Emulationcited by 4
  3. 2025SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QAcited by 3
  4. 2025A Kernel-based Resource-efficient Neural Surrogate for Multi-fidelity Prediction of Aerodynamic Fieldcited by 3
  5. 2026Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics -- Rotating Detonation Enginescited by 3
  6. 2025SMRS: advocating a unified reporting standard for surrogate models in the artificial intelligence eracited by 2
  7. 2024MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learningcited by 1
  8. 2026FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Modelscited by 1
  9. 2026Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurementscited by 1
  10. 2025Real-Time Performance Analysis of Multi-Fidelity Residual Physics-Informed Neural Process-Based State Estimation for Robotic Systemscited by 0
  11. 2025Neural surrogates for designing gravitational wave detectorscited by 0
  12. 2026Multi-Fidelity Physics-Informed Neural Networks with Bayesian Uncertainty Quantification and Adaptive Residual Learning for Efficient Solution of Parametric Partial Differential Equationscited by 0
1 more
  1. 2026Sparsely-Supervised Data Assimilation via Physics-Informed Schr"odinger Bridgecited by 0