Towards certification: A complete statistical validation pipeline for supervised learning in industry

• URL: http://arxiv.org/abs/2411.02075v1
• Date: Mon, 04 Nov 2024 13:27:32 GMT
• Title: Towards certification: A complete statistical validation pipeline for supervised learning in industry
• Authors: Lucas Lacasa, Abel Pardo, Pablo Arbelo, Miguel Sánchez, Pablo Yeste, Noelia Bascones, Alejandro Martínez-Cava, Gonzalo Rubio, Ignacio Gómez, Eusebio Valero, Javier de Vicente,
• Abstract summary: This paper outlines a complete validation pipeline that integrates deep learning, optimization and statistical methods. We illustrate the application of this pipeline in a realistic supervised problem arising in aerostructural design.
• Score: 32.97052224433939
• License:
• Abstract: Methods of Machine and Deep Learning are gradually being integrated into industrial operations, albeit at different speeds for different types of industries. The aerospace and aeronautical industries have recently developed a roadmap for concepts of design assurance and integration of neural network-related technologies in the aeronautical sector. This paper aims to contribute to this paradigm of AI-based certification in the context of supervised learning, by outlining a complete validation pipeline that integrates deep learning, optimization and statistical methods. This pipeline is composed by a directed graphical model of ten steps. Each of these steps is addressed by a merging key concepts from different contributing disciplines (from machine learning or optimization to statistics) and adapting them to an industrial scenario, as well as by developing computationally efficient algorithmic solutions. We illustrate the application of this pipeline in a realistic supervised problem arising in aerostructural design: predicting the likelikood of different stress-related failure modes during different airflight maneuvers based on a (large) set of features characterising the aircraft internal loads and geometric parameters.

Related papers

• Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach [0.7420433640907689]
  We encode domain-specific constraints, rules, and logical reasoning directly into the model's learning process. The proposed approach is flexible and applicable to both regression and classification tasks. The design allows for the automation of the loss function by simply updating the ASP rules.
  arXiv  Detail & Related papers  (2024-11-13T09:33:33Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• A Logic Programming Approach to Global Logistics in a Co-Design Environment [0.0]
  This paper considers the challenge of creating and optimizing a global logistics system for the construction of a passenger aircraft. The product in question is an aircraft, comprised of multiple components, manufactured at multiple sites worldwide. The goal is to find an optimal way to build the aircraft taking into consideration the requirements for its industrial system.
  arXiv  Detail & Related papers  (2023-08-30T09:06:34Z)
• PASTA: Pretrained Action-State Transformer Agents [10.654719072766495]
  Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains. Recent approaches involve pre-training transformer models on vast amounts of unlabeled data. In reinforcement learning, researchers have recently adapted these approaches, developing models pre-trained on expert trajectories.
  arXiv  Detail & Related papers  (2023-07-20T15:09:06Z)
• Unifying Synergies between Self-supervised Learning and Dynamic Computation [53.66628188936682]
  We present a novel perspective on the interplay between SSL and DC paradigms. We show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off.
  arXiv  Detail & Related papers  (2023-01-22T17:12:58Z)
• Investigation of Physics-Informed Deep Learning for the Prediction of Parametric, Three-Dimensional Flow Based on Boundary Data [0.0]
  We present a parameterized surrogate model for the prediction of three-dimensional flow fields in aerothermal vehicle simulations. The proposed physics-informed neural network (PINN) design is aimed at learning families of flow solutions according to a geometric variation.
  arXiv  Detail & Related papers  (2022-03-17T09:54:22Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines [48.7576911714538]
  The proposed approach is aimed to automate the design of composite machine learning pipelines. It designs the pipelines with a customizable graph-based structure, analyzes the obtained results, and reproduces them. The software implementation on this approach is presented as an open-source framework.
  arXiv  Detail & Related papers  (2021-06-26T23:19:06Z)
• Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning [49.367020832638794]
  The aerospace industry is poised to capitalize on big data and machine learning. Recent trends will be explored in context of critical challenges in design, manufacturing, verification and services.
  arXiv  Detail & Related papers  (2020-08-24T22:40:26Z)

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.