<div dir="ltr"><div class="gmail_default" style="font-family:verdana,sans-serif"><div class="gmail_default" style="font-family:verdana,sans-serif">Dear all, <br></div><div class="gmail_default" style="font-family:verdana,sans-serif"><br></div><div style="font-family:verdana,sans-serif" class="gmail_default">the
next speaker of the "very informal seminars" series is Massimiliano Martinelli (title and abstract of his talk are to be found below). <br></div><div style="font-family:verdana,sans-serif" class="gmail_default"><br></div><div style="font-family:verdana,sans-serif" class="gmail_default">Max's seminar is scheduled for<b> Monday, October 30, 4pm</b>, and it will take place in the <b>Conference Room</b> of <b>IMATI-CNR</b>.</div><div style="font-family:verdana,sans-serif" class="gmail_default"><br></div><div><div style="font-family:verdana,sans-serif" class="gmail_default">Should you need any further information, please feel free to contact us.</div><div style="font-family:verdana,sans-serif" class="gmail_default"><br></div><div style="font-family:verdana,sans-serif" class="gmail_default">Best regards, <br></div><div style="font-family:verdana,sans-serif" class="gmail_default"><br></div><div style="font-family:verdana,sans-serif" class="gmail_default">Andrea, Laura and Lorenzo<font color="#888888"><font color="#888888"><br></font></font></div></div></div><div><br></div><div><span style="font-family:verdana,sans-serif">Title:<br></span>
<span style="font-family:verdana,sans-serif"><br>
FTL: A new Functional Tensor Train Library written in RUST for Numerical <br>
Integration and Resolution of Partial Differential Equations<br></span>
<span style="font-family:verdana,sans-serif"><br>
Abstract:<br></span>
<span style="font-family:verdana,sans-serif"><br>
Originally, low-rank tensor decomposition algorithms were designed to <br>
approximate high-dimensional tensors. Due to its mathematical <br>
characteristics, Tensor-Train decomposition, a type of tensor <br>
decomposition that does not necessarily suffer from the curse of <br>
dimensionality, has garnered much interest during the past decade. In <br>
recent years, Function-Train decomposition, a continuous version of <br>
Tensor-Train decomposition, was introduced. This decomposition permits <br>
the approximation of high-dimensional functions without function <br>
sampling and provides an extensible framework for function integration <br>
and differentiation. In this talk, we present a new RUST-based library <br>
designed to provide functionality for Function-Train decomposition, and <br>
we also present some application examples (multidimensional quadrature, <br>
solution of ODE systems and PDEs)<font color="#888888"><br></font></span></div><div><br></div><div><br></div><div><br></div><span class="gmail_signature_prefix">-- </span><br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div><div><div><span style="font-family:verdana,sans-serif">Laura Spinolo<br></span></div><span style="font-family:verdana,sans-serif">IMATI-CNR<br></span></div><span style="font-family:verdana,sans-serif">via Ferrata 5, 27100 Pavia, Italy <br></span></div><span style="font-family:verdana,sans-serif">Web: <a href="http://arturo.imati.cnr.it/spinolo/" target="_blank">http://arturo.imati.cnr.it/spinolo/</a><br></span></div><span style="font-family:verdana,sans-serif">Email: <a href="mailto:spinolo@imati.cnr.it" target="_blank">spinolo@imati.cnr.it</a></span> <br></div></div></div></div></div></div></div></div></div></div></div></div></div>