Dataset for MRes Thesis: Probabilistic Operator Learning for Climate Model Parameterisation

<p>This dataset was collated for use in experiments presented in the MRes Thesis "Probabilistic Operator Learning for Climate Model Parameterisation" submitted to the University of Cambridge.<br><br>All data included was generated by other researchers, this is simply a subset to allow easy reproduction of the experiments contained in the work above.</p> <p>The data for the Burgers' equation (<a href="../api/records/12529654/draft/files/burgers_data_R10.mat/content" target="_blank" rel="noopener noreferrer">burgers_data_R10.mat</a>) and the Darcy Flow (<a href="../api/records/12529654/draft/files/piececonst_r421_N1024_smooth1.mat/content" target="_blank" rel="noopener noreferrer">piececonst_r421_N1024_smooth1.mat</a>, <a href="../api/records/12529654/draft/files/piececonst_r421_N1024_smooth2.mat/content" target="_blank" rel="noopener noreferrer">piececonst_r421_N1024_smooth2.mat</a>) experiments were generated by Lu et al. (2022). Creative Commons Attribution Non Commercial Share Alike 4.0 International applies.</p> <p>The data for the Helmholtz (<a href="../api/records/12529654/draft/files/Helmholtz_inputs.npy/content" target="_blank" rel="noopener noreferrer">Helmholtz_inputs.npy</a>, <a href="../api/records/12529654/draft/files/Helmholtz_outputs.npy/content" target="_blank" rel="noopener noreferrer">Helmholtz_outputs.npy</a>) and Navier-Stokes (<a href="../api/records/12529654/draft/files/NavierStokes_inputs.npy/content" target="_blank" rel="noopener noreferrer">NavierStokes_inputs.npy</a>, <a href="../api/records/12529654/draft/files/NavierStokes_outputs.npy/content" target="_blank" rel="noopener noreferrer">NavierStokes_outputs.npy</a>) experiments were generated by de Hoop et al. (2022). Creative Commons Attribution 4.0 International applies.</p> <p>References:</p> <div> <div> <div>1.&nbsp;Lu L, Meng X, Cai S, Mao Z, Goswami S, Zhang Z, et al. A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data. Computer Methods in Applied Mechanics and Engineering. 2022 Apr 1;393:114778.</div> <div>&nbsp;</div> <div> <div> <div> <div>2.&nbsp;de Hoop MV, Huang DZ, Null EQ, Stuart AM. The Cost-Accuracy Trade-Off in Operator Learning with Neural Networks. JML. 2022 Jun;1(3):299&ndash;341.</div> </div> </div> </div> </div> </div> <p>&nbsp;</p>