217 research outputs found
Neural ODEs with stochastic vector field mixtures
It was recently shown that neural ordinary differential equation models
cannot solve fundamental and seemingly straightforward tasks even with
high-capacity vector field representations. This paper introduces two other
fundamental tasks to the set that baseline methods cannot solve, and proposes
mixtures of stochastic vector fields as a model class that is capable of
solving these essential problems. Dynamic vector field selection is of critical
importance for our model, and our approach is to propagate component
uncertainty over the integration interval with a technique based on forward
filtering. We also formalise several loss functions that encourage desirable
properties on the trajectory paths, and of particular interest are those that
directly encourage fewer expected function evaluations. Experimentally, we
demonstrate that our model class is capable of capturing the natural dynamics
of human behaviour; a notoriously volatile application area. Baseline
approaches cannot adequately model this problem
Physics Informed Machine Learning of SPH: Machine Learning Lagrangian Turbulence
Smoothed particle hydrodynamics (SPH) is a mesh-free Lagrangian method for
obtaining approximate numerical solutions of the equations of fluid dynamics;
which has been widely applied to weakly- and strongly compressible turbulence
in astrophysics and engineering applications. We present a learn-able hierarchy
of parameterized and "physics-explainable" Lagrangian based fluid simulators
using both physics based parameters and Neural Networks (NNs) as universal
function approximators. This hierarchy of parameterized Lagrangian models
gradually introduces more SPH based structure, which we show improves
interpretability, generalizability (over larger ranges of time scales and Mach
numbers), preservation of physical symmetries (corresponding to conservation of
linear and angular momentum), and requires less training data. Our learning
algorithm develops a mixed mode approach, mixing forward and reverse mode
automatic differentiation with local sensitivity analyses to efficiently
perform gradient based optimization. We train this hierarchy on both weakly
compressible SPH and DNS data, and show that our physics informed learning
method is capable of: (a) solving inverse problems over the physically
interpretable parameter space, as well as over the space of NN parameters; (b)
learning Lagrangian statistics of turbulence (interpolation); (c) combining
Lagrangian trajectory based, probabilistic, and Eulerian field based loss
functions; (d) extrapolating beyond training sets into more complex regimes of
interest; (e) learning new parameterized smoothing kernels better suited to
weakly compressible DNS turbulence data
A General Framework for Uncertainty Quantification via Neural SDE-RNN
Uncertainty quantification is a critical yet unsolved challenge for deep
learning, especially for the time series imputation with irregularly sampled
measurements. To tackle this problem, we propose a novel framework based on the
principles of recurrent neural networks and neural stochastic differential
equations for reconciling irregularly sampled measurements. We impute
measurements at any arbitrary timescale and quantify the uncertainty in the
imputations in a principled manner. Specifically, we derive analytical
expressions for quantifying and propagating the epistemic and aleatoric
uncertainty across time instants. Our experiments on the IEEE 37 bus test
distribution system reveal that our framework can outperform state-of-the-art
uncertainty quantification approaches for time-series data imputations.Comment: 7 pages, 3 figure
Principled interpolation of Green's functions learned from data
We present a data-driven approach to mathematically model physical systems
whose governing partial differential equations are unknown, by learning their
associated Green's function. The subject systems are observed by collecting
input-output pairs of system responses under excitations drawn from a Gaussian
process. Two methods are proposed to learn the Green's function. In the first
method, we use the proper orthogonal decomposition (POD) modes of the system as
a surrogate for the eigenvectors of the Green's function, and subsequently fit
the eigenvalues, using data. In the second, we employ a generalization of the
randomized singular value decomposition (SVD) to operators, in order to
construct a low-rank approximation to the Green's function. Then, we propose a
manifold interpolation scheme, for use in an offline-online setting, where
offline excitation-response data, taken at specific model parameter instances,
are compressed into empirical eigenmodes. These eigenmodes are subsequently
used within a manifold interpolation scheme, to uncover other suitable
eigenmodes at unseen model parameters. The approximation and interpolation
numerical techniques are demonstrated on several examples in one and two
dimensions
- …