Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities

• URL: http://arxiv.org/abs/2111.06223v1
• Date: Sun, 7 Nov 2021 22:31:23 GMT
• Title: Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities
• Authors: Indranil Pan, Lachlan Mason, Omar Matar
• Abstract summary: Recent advances in machine learning, coupled with low-cost computation, have led to widespread multi-disciplinary research activity. Mechanistic models, based on physical equations, and purely data-driven statistical approaches represent two ends of the modelling spectrum. New hybrid, data-centric engineering approaches, leveraging the best of both worlds and integrating both simulations and data, are emerging as a powerful tool.
• Score: 1.3535770763481905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in machine learning, coupled with low-cost computation, availability of cheap streaming sensors, data storage and cloud technologies, has led to widespread multi-disciplinary research activity with significant interest and investment from commercial stakeholders. Mechanistic models, based on physical equations, and purely data-driven statistical approaches represent two ends of the modelling spectrum. New hybrid, data-centric engineering approaches, leveraging the best of both worlds and integrating both simulations and data, are emerging as a powerful tool with a transformative impact on the physical disciplines. We review the key research trends and application scenarios in the emerging field of integrating simulations, machine learning, and statistics. We highlight the opportunities that such an integrated vision can unlock and outline the key challenges holding back its realisation. We also discuss the bottlenecks in the translational aspects of the field and the long-term upskilling requirements of the existing workforce and future university graduates.

Related papers

• FinML-Chain: A Blockchain-Integrated Dataset for Enhanced Financial Machine Learning [2.0695662173473206]
  We present a framework for integrating high-frequency on-chain data with low-frequency off-chain data. This framework generates modular datasets for analyzing economic mechanisms such as the Transaction Fee Mechanism. We demonstrate the framework's ability to produce datasets that advance financial research and improve understanding of blockchain-driven systems.
  arXiv  Detail & Related papers  (2024-11-25T10:55:11Z)
• A spectrum of physics-informed Gaussian processes for regression in engineering [0.0]
  Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven approach. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data.
  arXiv  Detail & Related papers  (2023-09-19T14:39:03Z)
• Addressing computational challenges in physical system simulations with machine learning [0.0]
  We present a machine learning-based data generator framework tailored to aid researchers who utilize simulations to examine various physical systems or processes. Our approach involves a two-step process: first, we train a supervised predictive model using a limited simulated dataset to predict simulation outcomes. Subsequently, a reinforcement learning agent is trained to generate accurate, simulation-like data by leveraging the supervised model.
  arXiv  Detail & Related papers  (2023-05-16T17:31:50Z)
• Machine Learning for Synthetic Data Generation: A Review [23.073056971997715]
  This paper reviews existing studies that employ machine learning models for the purpose of generating synthetic data. The review encompasses various perspectives, starting with the applications of synthetic data generation, spanning computer vision, speech, natural language processing, healthcare, and business domains. The paper also addresses the crucial aspects of privacy and fairness concerns related to synthetic data generation.
  arXiv  Detail & Related papers  (2023-02-08T13:59:31Z)
• Vision+X: A Survey on Multimodal Learning in the Light of Data [64.03266872103835]
  multimodal machine learning that incorporates data from various sources has become an increasingly popular research area. We analyze the commonness and uniqueness of each data format mainly ranging from vision, audio, text, and motions. We investigate the existing literature on multimodal learning from both the representation learning and downstream application levels.
  arXiv  Detail & Related papers  (2022-10-05T13:14:57Z)
• Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions [68.6358773622615]
  This paper provides an overview of the computational and theoretical foundations of multimodal machine learning. We propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches.
  arXiv  Detail & Related papers  (2022-09-07T19:21:19Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis [78.26022688167133]
  We present a large-scale benchmark dataset for vision-driven robotic grasping via physics-based metaverse synthesis. The proposed dataset contains 100,000 images and 25 different object types. We also propose a new layout-weighted performance metric alongside the dataset for evaluating object detection and segmentation performance.
  arXiv  Detail & Related papers  (2021-12-29T17:23:24Z)
• INTERN: A New Learning Paradigm Towards General Vision [117.3343347061931]
  We develop a new learning paradigm named INTERN. By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability. In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data.
  arXiv  Detail & Related papers  (2021-11-16T18:42:50Z)
• 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)
• Graph signal processing for machine learning: A review and new perspectives [57.285378618394624]
  We review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. We discuss exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. We provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other.
  arXiv  Detail & Related papers  (2020-07-31T13:21:33Z)

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.