5 research outputs found
Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators
Uncertainty quantification (UQ) in scientific machine learning (SciML)
becomes increasingly critical as neural networks (NNs) are being widely adopted
in addressing complex problems across various scientific disciplines.
Representative SciML models are physics-informed neural networks (PINNs) and
neural operators (NOs). While UQ in SciML has been increasingly investigated in
recent years, very few works have focused on addressing the uncertainty caused
by the noisy inputs, such as spatial-temporal coordinates in PINNs and input
functions in NOs. The presence of noise in the inputs of the models can pose
significantly more challenges compared to noise in the outputs of the models,
primarily due to the inherent nonlinearity of most SciML algorithms. As a
result, UQ for noisy inputs becomes a crucial factor for reliable and
trustworthy deployment of these models in applications involving physical
knowledge. To this end, we introduce a Bayesian approach to quantify
uncertainty arising from noisy inputs-outputs in PINNs and NOs. We show that
this approach can be seamlessly integrated into PINNs and NOs, when they are
employed to encode the physical information. PINNs incorporate physics by
including physics-informed terms via automatic differentiation, either in the
loss function or the likelihood, and often take as input the spatial-temporal
coordinate. Therefore, the present method equips PINNs with the capability to
address problems where the observed coordinate is subject to noise. On the
other hand, pretrained NOs are also commonly employed as equation-free
surrogates in solving differential equations and Bayesian inverse problems, in
which they take functions as inputs. The proposed approach enables them to
handle noisy measurements for both input and output functions with UQ
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning
We address two major challenges in scientific machine learning (SciML):
interpretability and computational efficiency. We increase the interpretability
of certain learning processes by establishing a new theoretical connection
between optimization problems arising from SciML and a generalized Hopf
formula, which represents the viscosity solution to a Hamilton-Jacobi partial
differential equation (HJ PDE) with time-dependent Hamiltonian. Namely, we show
that when we solve certain regularized learning problems with integral-type
losses, we actually solve an optimal control problem and its associated HJ PDE
with time-dependent Hamiltonian. This connection allows us to reinterpret
incremental updates to learned models as the evolution of an associated HJ PDE
and optimal control problem in time, where all of the previous information is
intrinsically encoded in the solution to the HJ PDE. As a result, existing HJ
PDE solvers and optimal control algorithms can be reused to design new
efficient training approaches for SciML that naturally coincide with the
continual learning framework, while avoiding catastrophic forgetting. As a
first exploration of this connection, we consider the special case of linear
regression and leverage our connection to develop a new Riccati-based
methodology for solving these learning problems that is amenable to continual
learning applications. We also provide some corresponding numerical examples
that demonstrate the potential computational and memory advantages our
Riccati-based approach can provide
Cell transcriptomic atlas of the non-human primate Macaca fascicularis.
Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding
Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland
Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for
technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene
expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi
from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing
reagents. This work was supported by the Shenzhen Basic Research Project for Excellent
Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics
(ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In
addition, L.L. was supported by the National Natural Science Foundation of China (31900466),
Y. Hou was supported by the Natural Science Foundation of Guangdong Province
(2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award
(419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences
(XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science
joint research project (GJHZ2093), the National Natural Science Foundation of China
(92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation
(2021B1515120075). M.L. was supported by the National Key Research and Development
Program of China (2021YFC2600200).S
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes
Quantifying biomechanical properties of the human vasculature could deepen
our understanding of cardiovascular diseases. Standard nonlinear regression in
constitutive modeling requires considerable high-quality data and an explicit
form of the constitutive model as prior knowledge. By contrast, we propose a
novel approach that combines generative deep learning with Bayesian inference
to efficiently infer families of constitutive relationships in data-sparse
regimes. Inspired by the concept of functional priors, we develop a generative
adversarial network (GAN) that incorporates a neural operator as the generator
and a fully-connected neural network as the discriminator. The generator takes
a vector of noise conditioned on measurement data as input and yields the
predicted constitutive relationship, which is scrutinized by the discriminator
in the following step. We demonstrate that this framework can accurately
estimate means and standard deviations of the constitutive relationships of the
murine aorta using data collected either from model-generated synthetic data or
ex vivo experiments for mice with genetic deficiencies. In addition, the
framework learns priors of constitutive models without explicitly knowing their
functional form, providing a new model-agnostic approach to learning hidden
constitutive behaviors from data
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems
Understanding real-world dynamical phenomena remains a challenging task.
Across various scientific disciplines, machine learning has advanced as the
go-to technology to analyze nonlinear dynamical systems, identify patterns in
big data, and make decision around them. Neural networks are now consistently
used as universal function approximators for data with underlying mechanisms
that are incompletely understood or exceedingly complex. However, neural
networks alone ignore the fundamental laws of physics and often fail to make
plausible predictions. Here we integrate data, physics, and uncertainties by
combining neural networks, physics-informed modeling, and Bayesian inference to
improve the predictive potential of traditional neural network models. We embed
the physical model of a damped harmonic oscillator into a fully-connected
feed-forward neural network to explore a simple and illustrative model system,
the outbreak dynamics of COVID-19. Our Physics-Informed Neural Networks can
seamlessly integrate data and physics, robustly solve forward and inverse
problems, and perform well for both interpolation and extrapolation, even for a
small amount of noisy and incomplete data. At only minor additional cost, they
can self-adaptively learn the weighting between data and physics. Combined with
Bayesian Neural Networks, they can serve as priors in a Bayesian Inference, and
provide credible intervals for uncertainty quantification. Our study reveals
the inherent advantages and disadvantages of Neural Networks, Bayesian
Inference, and a combination of both and provides valuable guidelines for model
selection. While we have only demonstrated these approaches for the simple
model problem of a seasonal endemic infectious disease, we anticipate that the
underlying concepts and trends generalize to more complex disease conditions
and, more broadly, to a wide variety of nonlinear dynamical systems