1,418 research outputs found
Deep Probabilistic Surrogate Networks for Universal Simulator Approximation
We present a framework for automatically structuring and training fast,
approximate, deep neural surrogates of existing stochastic simulators. Unlike
traditional approaches to surrogate modeling, our surrogates retain the
interpretable structure of the reference simulators. The particular way we
achieve this allows us to replace the reference simulator with the surrogate
when undertaking amortized inference in the probabilistic programming sense.
The fidelity and speed of our surrogates allow for not only faster "forward"
stochastic simulation but also for accurate and substantially faster inference.
We support these claims via experiments that involve a commercial
composite-materials curing simulator. Employing our surrogate modeling
technique makes inference an order of magnitude faster, opening up the
possibility of doing simulator-based, non-invasive, just-in-time parts quality
testing; in this case inferring safety-critical latent internal temperature
profiles of composite materials undergoing curing from surface temperature
profile measurements
Mining gold from implicit models to improve likelihood-free inference
Simulators often provide the best description of real-world phenomena.
However, they also lead to challenging inverse problems because the density
they implicitly define is often intractable. We present a new suite of
simulation-based inference techniques that go beyond the traditional
Approximate Bayesian Computation approach, which struggles in a
high-dimensional setting, and extend methods that use surrogate models based on
neural networks. We show that additional information, such as the joint
likelihood ratio and the joint score, can often be extracted from simulators
and used to augment the training data for these surrogate models. Finally, we
demonstrate that these new techniques are more sample efficient and provide
higher-fidelity inference than traditional methods.Comment: Code available at
https://github.com/johannbrehmer/simulator-mining-example . v2: Fixed typos.
v3: Expanded discussion, added Lotka-Volterra example. v4: Improved clarit
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
This work proposes ''jointly amortized neural approximation'' (JANA) of
intractable likelihood functions and posterior densities arising in Bayesian
surrogate modeling and simulation-based inference. We train three complementary
networks in an end-to-end fashion: 1) a summary network to compress individual
data points, sets, or time series into informative embedding vectors; 2) a
posterior network to learn an amortized approximate posterior; and 3) a
likelihood network to learn an amortized approximate likelihood. Their
interaction opens a new route to amortized marginal likelihood and posterior
predictive estimation -- two important ingredients of Bayesian workflows that
are often too expensive for standard methods. We benchmark the fidelity of JANA
on a variety of simulation models against state-of-the-art Bayesian methods and
propose a powerful and interpretable diagnostic for joint calibration. In
addition, we investigate the ability of recurrent likelihood networks to
emulate complex time series models without resorting to hand-crafted summary
statistics
A survey on policy search algorithms for learning robot controllers in a handful of trials
Most policy search algorithms require thousands of training episodes to find
an effective policy, which is often infeasible with a physical robot. This
survey article focuses on the extreme other end of the spectrum: how can a
robot adapt with only a handful of trials (a dozen) and a few minutes? By
analogy with the word "big-data", we refer to this challenge as "micro-data
reinforcement learning". We show that a first strategy is to leverage prior
knowledge on the policy structure (e.g., dynamic movement primitives), on the
policy parameters (e.g., demonstrations), or on the dynamics (e.g.,
simulators). A second strategy is to create data-driven surrogate models of the
expected reward (e.g., Bayesian optimization) or the dynamical model (e.g.,
model-based policy search), so that the policy optimizer queries the model
instead of the real system. Overall, all successful micro-data algorithms
combine these two strategies by varying the kind of model and prior knowledge.
The current scientific challenges essentially revolve around scaling up to
complex robots (e.g., humanoids), designing generic priors, and optimizing the
computing time.Comment: 21 pages, 3 figures, 4 algorithms, accepted at IEEE Transactions on
Robotic
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks
Several fundamental problems in science and engineering consist of global
optimization tasks involving unknown high-dimensional (black-box) functions
that map a set of controllable variables to the outcomes of an expensive
experiment. Bayesian Optimization (BO) techniques are known to be effective in
tackling global optimization problems using a relatively small number objective
function evaluations, but their performance suffers when dealing with
high-dimensional outputs. To overcome the major challenge of dimensionality,
here we propose a deep learning framework for BO and sequential decision making
based on bootstrapped ensembles of neural architectures with randomized priors.
Using appropriate architecture choices, we show that the proposed framework can
approximate functional relationships between design variables and quantities of
interest, even in cases where the latter take values in high-dimensional vector
spaces or even infinite-dimensional function spaces. In the context of BO, we
augmented the proposed probabilistic surrogates with re-parameterized Monte
Carlo approximations of multiple-point (parallel) acquisition functions, as
well as methodological extensions for accommodating black-box constraints and
multi-fidelity information sources. We test the proposed framework against
state-of-the-art methods for BO and demonstrate superior performance across
several challenging tasks with high-dimensional outputs, including a
constrained optimization task involving shape optimization of rotor blades in
turbo-machinery.Comment: 18 pages, 8 figure
RiskNet: neural risk assessment in networks of unreliable resources
We propose a graph neural network (GNN)-based method to predict the distribution of penalties induced by outages in communication networks, where connections are protected by resources shared between working and backup paths. The GNN-based algorithm is trained only with random graphs generated on the basis of the Barabási–Albert model. However, the results obtained show that we can accurately model the penalties in a wide range of existing topologies. We show that GNNs eliminate the need to simulate complex outage scenarios for the network topologies under study—in practice, the entire time of path placement evaluation based on the prediction is no longer than 4 ms on modern hardware. In this way, we gain up to 12 000 times in speed improvement compared to calculations based on simulations.This work was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University of Science and Technology (P.B., P.C.) and by the PL-Grid Infrastructure (K.R.).Peer ReviewedPostprint (published version
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