Case for a unified surrogate modelling framework in the age of AI

• URL: http://arxiv.org/abs/2502.06753v1
• Date: Mon, 10 Feb 2025 18:31:15 GMT
• Title: Case for a unified surrogate modelling framework in the age of AI
• Authors: Elizaveta Semenova,
• Abstract summary: We argue for the urgent need for a unified framework to guide the development and evaluation of surrogate models. This paper seeks to improve the reliability of surrogate modelling, foster cross-disciplinary knowledge transfer, and, as a result, accelerate scientific progress.
• Score: 0.0
• License:
• Abstract: Surrogate models are widely used in natural sciences, engineering, and machine learning to approximate complex systems and reduce computational costs. However, the current landscape lacks standardisation across key stages of the pipeline, including data collection, sampling design, model class selection, evaluation metrics, and downstream task performance analysis. This fragmentation limits reproducibility, reliability, and cross-domain applicability. The issue has only been exacerbated by the AI revolution and a new suite of surrogate model classes that it offers. In this position paper, we argue for the urgent need for a unified framework to guide the development and evaluation of surrogate models. We outline essential steps for constructing a comprehensive pipeline and discuss alternative perspectives, such as the benefits of domain-specific frameworks. By advocating for a standardised approach, this paper seeks to improve the reliability of surrogate modelling, foster cross-disciplinary knowledge transfer, and, as a result, accelerate scientific progress.

Related papers

• LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging [10.33844295243509]
  We propose a unified framework for model merging based on low-rank estimation of task vectors without the need for access to the base model, named textscLoRE-Merging. Our approach is motivated by the observation that task vectors from fine-tuned models frequently exhibit a limited number of dominant singular values, making low-rank estimations less prone to interference.
  arXiv  Detail & Related papers  (2025-02-15T10:18:46Z)
• Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks [4.515998639772672]
  Surrogate models are frequently employed as efficient substitutes for the costly execution of real-world processes. This study focuses on transferring non-differentiable surrogate models from a source function to a target function. We assume their domains are related by an unknown affine transformation, using only a limited amount of transfer data points evaluated on the target.
  arXiv  Detail & Related papers  (2025-01-23T18:44:25Z)
• Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation [1.912429179274357]
  This paper introduces a novel two-stage machine learning-based surrogate modeling framework to address inverse problems in scientific and engineering fields. In the first stage, a machine learning model termed the "learner" identifies a limited set of candidates within the input design space whose predicted outputs closely align with desired outcomes. In the second stage, a separate surrogate model, functioning as an "evaluator," is employed to assess the reduced candidate space generated in the first stage.
  arXiv  Detail & Related papers  (2024-01-04T00:25:12Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Refined Mechanism Design for Approximately Structured Priors via Active Regression [50.71772232237571]
  We consider the problem of a revenue-maximizing seller with a large number of items for sale to $n$ strategic bidders. It is well-known that optimal and even approximately-optimal mechanisms for this setting are notoriously difficult to characterize or compute.
  arXiv  Detail & Related papers  (2023-10-11T20:34:17Z)
• REX: Rapid Exploration and eXploitation for AI Agents [103.68453326880456]
  We propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance.
  arXiv  Detail & Related papers  (2023-07-18T04:26:33Z)
• GLUECons: A Generic Benchmark for Learning Under Constraints [102.78051169725455]
  In this work, we create a benchmark that is a collection of nine tasks in the domains of natural language processing and computer vision. We model external knowledge as constraints, specify the sources of the constraints for each task, and implement various models that use these constraints.
  arXiv  Detail & Related papers  (2023-02-16T16:45:36Z)
• Slimmable Domain Adaptation [112.19652651687402]
  We introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank. Our framework surpasses other competing approaches by a very large margin on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-06-14T06:28:04Z)
• Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning [65.268245109828]
  In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models. Deep learning in resource-limited domains still faces multiple challenges including (i) limited data, (ii) constrained model development cost, and (iii) lack of adequate pre-trained models for effective finetuning. Model reprogramming enables resource-efficient cross-domain machine learning by repurposing a well-developed pre-trained model from a source domain to solve tasks in a target domain without model finetuning.
  arXiv  Detail & Related papers  (2022-02-22T02:33:54Z)
• Towards Robust and Reliable Algorithmic Recourse [11.887537452826624]
  We propose a novel framework, RObust Algorithmic Recourse (ROAR), that leverages adversarial training for finding recourses that are robust to model shifts. We also carry out detailed theoretical analysis which underscores the importance of constructing recourses that are robust to model shifts.
  arXiv  Detail & Related papers  (2021-02-26T17:38:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.