A Data-Driven Approach to Morphogenesis under Structural Instability

• URL: http://arxiv.org/abs/2308.11846v1
• Date: Wed, 23 Aug 2023 00:51:43 GMT
• Title: A Data-Driven Approach to Morphogenesis under Structural Instability
• Authors: Yingjie Zhao and Zhiping Xu
• Abstract summary: We propose a data-driven approach to understand and predict morphological complexities. A machine-learning framework is proposed based on the physical modeling of morphogenesis triggered by internal or external forcing.
• Score: 1.223779595809275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Morphological development into evolutionary patterns under structural instability is ubiquitous in living systems and often of vital importance for engineering structures. Here we propose a data-driven approach to understand and predict their spatiotemporal complexities. A machine-learning framework is proposed based on the physical modeling of morphogenesis triggered by internal or external forcing. Digital libraries of structural patterns are constructed from the simulation data, which are then used to recognize the abnormalities, predict their development, and assist in risk assessment and prognosis. The capabilities to identify the key bifurcation characteristics and predict the history-dependent development from the global and local features are demonstrated by examples of brain growth and aerospace structural design, which offer guidelines for disease diagnosis/prognosis and instability-tolerant design.

Related papers

• No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biology [5.925258390690544]
  We argue that genuine "foundation models" of regulatory biology will remain out of reach unless guided by frameworks that integrate mechanistic insight with principled experimental design. We present one such ground-up, semi-mechanistic framework that unifies perturbation-based experimental designs.
  arXiv  Detail & Related papers  (2025-01-31T14:43:16Z)
• NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models [6.75152379258166]
  We present NeuralPLexer3, a flow-based generative model that achieves state-of-the-art prediction accuracy on key biomolecular interaction types. Examined through newly developed benchmarking strategies, NeuralPLexer3 excels in vital areas that are crucial to structure-based drug design.
  arXiv  Detail & Related papers  (2024-12-14T08:28:45Z)
• Predictive Pattern Recognition Techniques Towards Spatiotemporal Representation of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review [0.0]
  This review explores state-of-the-art predictive pattern recognition techniques. We focus on the probabilistic modeling of plant traits and the integration of dynamic environmental interactions. Key topics include regressions and neural network-based representation models for the task of forecasting.
  arXiv  Detail & Related papers  (2024-12-13T20:22:35Z)
• Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation [73.81105275628751]
  Finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms. We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds. Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel adaptive priors.
  arXiv  Detail & Related papers  (2024-07-26T10:29:16Z)
• Machine learning for structural design models of continuous beam systems via influence zones [3.284878354988896]
  This work develops a machine learned structural design model for continuous beam systems from the inverse problem perspective. The aim of this approach is to conceptualise a non-iterative structural design model that predicts cross-section requirements for continuous beam systems of arbitrary system size.
  arXiv  Detail & Related papers  (2024-03-14T14:53:18Z)
• On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games [55.2480439325792]
  In a sequential decision-making problem, the information structure is the description of how events in the system occurring at different points in time affect each other. By contrast, real-world sequential decision-making problems typically involve a complex and time-varying interdependence of system variables. We formalize a novel reinforcement learning model which explicitly represents the information structure.
  arXiv  Detail & Related papers  (2024-03-01T21:28:19Z)
• Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties [0.0]
  Several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. We develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science.
  arXiv  Detail & Related papers  (2023-09-01T07:29:44Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• Safe AI for health and beyond -- Monitoring to transform a health service [51.8524501805308]
  We will assess the infrastructure required to monitor the outputs of a machine learning algorithm. We will present two scenarios with examples of monitoring and updates of models.
  arXiv  Detail & Related papers  (2023-03-02T17:27:45Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection [0.618778092044887]
  Currently used AI-based data-driven methods for damage diagnostics and prognostics are centered on historical data of the structures. In this study, a variant of Generative Adversarial Networks (GAN), Cycle-Consistent Wasserstein Deep Convolutional GAN with Gradient Penalty (CycleWDCGAN-GP) model is used. The outcomes of this study demonstrate that the proposed model can accurately generate the possible future responses of a structure for potential future damaged conditions.
  arXiv  Detail & Related papers  (2022-02-16T02:31:38Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)
• Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networks [3.7814216736076434]
  We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task. Our framework is modular and consists of several computing blocks that perform fundamental shape processing tasks. We propose a discriminative model for disease classification and age regression, as well as a generative model for the accruate reconstruction of shapes.
  arXiv  Detail & Related papers  (2020-10-02T07:37:40Z)
• Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities [0.0]
  We propose a general framework to combine structured regression models and deep neural networks into a unifying network architecture. We demonstrate the framework's efficacy in numerical experiments and illustrate its special merits in benchmarks and real-world applications.
  arXiv  Detail & Related papers  (2020-02-13T21:01:26Z)

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.