Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences

Abstract

Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, classical machine learning techniques often ignore the fundamental laws of physics and result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large data sets from different sources and different levels of resolution. We show how machine learning and multiscale modeling can complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We critically review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.

References in corpus (22)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Solving high-dimensional partial differential equations using deep learning
• Turbulence Modeling in the Age of Data
• Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
• Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
• Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
• Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
• Machine Learning of Linear Differential Equations using Gaussian Processes
• Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
• Inferring solutions of differential equations using noisy multi-fidelity data
• Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
• Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings
• Sparse Identification of Nonlinear Dynamics for Rapid Model Recovery
• Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions
• Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics
• Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data
• Variational system identification of the partial differential equations governing pattern-forming physics: Inference under varying fidelity and noise
• Simulation assisted machine learning
• Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks
• Model selection for hybrid dynamical systems via sparse regression
• Active learning of constitutive relation from mesoscopic dynamics for macroscopic modeling of non-Newtonian flows
• Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models

Cited by in corpus (40)

• Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons
• A new family of Constitutive Artificial Neural Networks towards automated model discovery
• A Physics-Guided Neural Network Framework for Elastic Plates: Comparison of Governing Equations-Based and Energy-Based Approaches
• A Physics-Informed Deep Learning Paradigm for Car-Following Models
• Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems
• Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
• Physics-guided deep learning framework for predictive modeling of the Reynolds stress anisotropy
• Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems
• Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review
• Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modeling
• Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)
• Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics
• Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
• Physics-informed Neural Network Estimation of Material Properties in Soft Tissue Nonlinear Biomechanical Models
• Mining social media data for biomedical signals and health-related behavior
• Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data
• Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network
• Predicting the Mechanical Properties of Biopolymer Gels Using Neural Networks Trained on Discrete Fiber Network Data
• Condensed Matter Systems Exposed to Radiation: Multiscale Theory, Simulations, and Experiment
• Innovations in Integrating Machine Learning and Agent-Based Modeling of Biomedical Systems
• Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
• Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
• Towards out of distribution generalization for problems in mechanics
• Sparse Identification of Slow Timescale Dynamics
• Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
• Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
• Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century
• Rapid Estimation of Left Ventricular Contractility with a Physics-Informed Neural Network Inverse Modeling Approach
• Certified machine learning: A posteriori error estimation for physics-informed neural networks
• Emergent properties and the multiscale characterization challenge in condensed matter, from crystals to complex materials: a Review
• Error Approximation and Bias Correction in Dynamic Problems using a Recurrent Neural Network/Finite Element Hybrid Model
• Performance Comparison for Scientific Computations on the Edge via Relative Performance
• Hierarchical deep learning-based adaptive time-stepping scheme for multiscale simulations
• Swift progress for robots over complex terrain
• Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems
• Multiscale modeling of brain network organization
• Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation
• Exploring the potential of transfer learning for metamodels of heterogeneous material deformation
• A robust shape model for blood vessels analysis
• Machine-learning custom-made basis functions for partial differential equations