Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Suppressing simulation bias using multi-modal data   Many problems in science and engineering require making predictions based on
few observations. To build a robust predictive model, these sparse data may
need to be augmented with simulated data, especially when the design space is
multi-dimensional. Simulations, however, often suffer from an inherent bias.
Estimation of this bias may be poorly constrained not only because of data
sparsity, but also because traditional predictive models fit only one type of
observed outputs, such as scalars or images, instead of all available output
data modalities, which might have been acquired and simulated at great cost. To
break this limitation and open up the path for multi-modal calibration, we
propose to combine a novel, transfer learning technique for suppressing the
bias with recent developments in deep learning, which allow building predictive
models with multi-modal outputs. First, we train an initial neural network
model on simulated data to learn important correlations between different
output modalities and between simulation inputs and outputs. Then, the model is
partially retrained, or transfer learned, to fit the experiments; a method that
has never been implemented in this type of architecture. Using fewer than 10
inertial confinement fusion experiments for training, transfer learning
systematically improves the simulation predictions while a simple output
calibration, which we design as a baseline, makes the predictions worse. We
also offer extensive cross-validation with real and carefully designed
synthetic data. The method described in this paper can be applied to a wide
range of problems that require transferring knowledge from simulations to the
domain of experiments.

22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int