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<span style="font-family:Calibri,Helvetica,sans-serif; font-size:12pt">Dear all,</span><br>
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<span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Hope everyone is having a nice Monday!</span><br>
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<span style="font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; color: rgb(0, 0, 0);">At the S3RI seminar this Thursday (25 November) at 2 PM, we have Dr Lulu Kang </span><span style="font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; color: rgb(0, 0, 0);">(</span><span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif"><span style="text-align: center; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255); font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; display: inline !important;">Illinois
Institute of Technology</span></span><span style="font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; color: rgb(0, 0, 0);">) giving a talk on</span><br>
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<span style="margin: 0px; font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">"</span><span style="margin: 0px; font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">Gaussian Process Assisted Active
Learning of Physical Laws.</span><span style="margin: 0px; font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">"</span></div>
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">P</span><span style="font-family: Calibri, Helvetica, sans-serif; font-size: 12pt; color: rgb(0, 0, 0);">lease see below for the details of the talk and how to
join the seminar online.</span></div>
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<span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: rgb(0, 0, 0);">Have a great week, and hope to see you at the seminar on Thursday!</span><span style="margin: 0px; font-size: 12pt; font-family: "Times New Roman", serif; color: black;"></span></p>
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<span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: rgb(0, 0, 0);">Kind regards,</span><span style="margin: 0px; font-size: 12pt; font-family: "Times New Roman", serif; color: black;"></span></p>
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</span><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: rgb(0, 0, 0);">Jessie</span><span style="margin: 0px; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif; background-color: white; color: rgb(0, 0, 0);"><br>
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<div style="font-size:14px"><em><strong><span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Gaussian Process Assisted Active Learning of Physical Laws</span></strong></em></div>
<div style=""><span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">Lulu Kang (Illinois Institute of Technology)</span><span style="font-size:15px"><br>
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</span><span style="font-size:12pt; font-family:Calibri,Helvetica,sans-serif">In many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential challenge. It is also a critical step in
understanding the physical phenomena and prediction of the future behaviors of the systems. However, in many cases, it is expensive or time-consuming to collect experimental data. This article provides an active learning approach to estimate the unknown differential
equations accurately with reduced experimental data size. We propose an adaptive design criterion combining the D-optimality and the maximin space-filling criterion. In contrast to active learning for other regression models, the D-optimality here requires
the unknown solution of the differential equations and derivatives of the solution. We estimate the Gaussian process (GP) regression models from the available experimental data and use them as the surrogates of these unknown solution functions. The derivatives
of the estimated GP models are derived and used to substitute the derivatives of the solution. Variable selection-based regression methods are used to learn the differential equations from the experimental data. Through multiple case studies, we demonstrate
the proposed approach outperforms the D-optimality and the maximin space-filling design alone in terms of model accuracy and data economy.</span></div>
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