deploy: e493227

Author: pancetta

references/index.html

@@ -218,101 +218,6 @@ <h2>Statistics</h2>
 <h2 id="year2024" class="year">2024 <span class="count-stat"></span></h2>
 
 <ol class="bibliography"><li><div class="bibtex-entry-container">
-  <div>
-    <div class="col-md-3 col-sm-12 bibtex-ref-meta hidden">
-      <div class="row">
-        <div class="col-md-12 ref-label label label-default">
-          BetckeEtAl2024
-        </div>
-      </div>
-    </div>
-
-    <div class="bibtex-ref-entry">
-      <span id="BetckeEtAl2024">M. M. Betcke, L. M. Kreusser, and D. Murari, “Parallel-in-Time Solutions with Random Projection Neural Networks,” arXiv:2408.09756v1 [math.NA], 2024 [Online]. Available at: <a href="http://arxiv.org/abs/2408.09756v1" target="_blank">http://arxiv.org/abs/2408.09756v1</a></span>
-    </div>
-
-
-    <div class="row mt-4">
-      <div class="btn-group btn-group-xs btn-group-justified" role="group">
-        <div class="btn-group btn-group-xs" role="group">
-          
-          <button class="button" data-toggle="modal" data-target="#modalBetckeEtAl2024Bibtex" aria-expanded="false"
-                  aria-controls="modalBetckeEtAl2024Bibtex">
-            BibTeX
-          </button>
-          
-        </div>
-        <div class="btn-group btn-group-xs" role="group">
-          
-          <button class="button" data-toggle="modal" data-target="#modalBetckeEtAl2024Abstract" aria-expanded="false"
-                  aria-controls="modalBetckeEtAl2024Abstract">
-            Abstract
-          </button>
-          
-        </div>
-      </div>
-    </div>
-  </div>
-  
-  <div class="modal" id="modalBetckeEtAl2024Bibtex" tabindex="-1" role="dialog"
-    aria-labelledby="modalBetckeEtAl2024BibtexTitle" aria-hidden="true">
-    <div class="modal-background">
-      <div class="modal-card">
-        <div class="modal-content">
-          <header class="modal-card-head">
-            <div class="modal-card-head-content">
-              <div>
-                <p class="modal-card-title" id="modalBetckeEtAl2024BibtexTitle">
-                  BibTeX entry <code>BetckeEtAl2024</code>
-                </p>
-              </div>
-              <button type="button" class="delete" data-dismiss="modal" aria-label="Close"><span
-                  aria-hidden="true">&times;</span></button>
-            </div>
-          </header>
-          <section class="modal-card-body">
-            <pre class="abstract">@unpublished{BetckeEtAl2024,
-  author = {Betcke, Marta M. and Kreusser, Lisa Maria and Murari, Davide},
-  howpublished = {arXiv:2408.09756v1 [math.NA]},
-  title = {Parallel-in-Time Solutions with Random Projection Neural Networks},
-  url = {http://arxiv.org/abs/2408.09756v1},
-  year = {2024}
-}
-</pre>
-          </section>
-        </div>
-      </div>
-    </div>
-  </div>
-  
-  
-  <div class="modal" id="modalBetckeEtAl2024Abstract" tabindex="-1" role="dialog"
-       aria-labelledby="modalBetckeEtAl2024AbstractTitle" aria-hidden="true">
-    <div class="modal-background">
-      <div class="modal-card">
-        <div class="modal-content">
-          <header class="modal-card-head">
-            <div class="modal-card-head-content">
-              <div>
-                <p class="modal-card-title" id="modalBetckeEtAl2024AbstractTitle">
-                  Abstract for BibTeX entry <code>BetckeEtAl2024</code>
-                </p>
-              </div>
-              <button type="button" class="delete" data-dismiss="modal" aria-label="Close"><span
-                      aria-hidden="true">&times;</span></button>
-            </div>
-          </header>
-          <section class="modal-card-body">
-            This paper considers one of the fundamental parallel-in-time methods for the solution of ordinary differential equations, Parareal, and extends it by adopting a neural network as a coarse propagator. We provide a theoretical analysis of the convergence properties of the proposed algorithm and show its effectiveness for several examples, including Lorenz and Burgers’ equations. In our numerical simulations, we further specialize the underpinning neural architecture to Random Projection Neural Networks (RPNNs), a 2-layer neural network where the first layer weights are drawn at random rather than optimized. This restriction substantially increases the efficiency of fitting RPNN’s weights in comparison to a standard feedforward network without negatively impacting the accuracy, as demonstrated in the SIR system example.
-          </section>
-        </div>
-      </div>
-    </div>
-  </div>
-  
-</div>
-</li>
-<li><div class="bibtex-entry-container">
   <div>
     <div class="col-md-3 col-sm-12 bibtex-ref-meta hidden">
       <div class="row">