The announcement on the CUASHI site is here.
All talks take place on Fridays at 1:00 p.m. ET: Registration is free! You must register for the series in order to attend. To register, click here.
This is the foreseen schedule:
- March 29, 2019: Machine Learning & Information Theory for Land Model Benchmarking & Process Diagnostics | Grey Nearing, University of Alabama
- April 5, 2019: Long Short-Term Memory (LSTM) networks for rainfall-runoff modeling | Frederik Kratzert, Johannes Kepler University
- April 12, 2019: Use deep convolutional neural nets to learn patterns of mismatch between a land surface model and GRACE satellite | Alex Sun, University of Texas at Austin
- April 19, 2019: Long-term projections of soil moisture using deep learning and SMAP data with aleatoric and epistemic uncertainty estimates | Chaopeng Shen, Pennsylvania State University
- April 26, 2019: Exploring deep neural networks to retrieve rain and snow in high latitudes using multi-sensor and reanalysis data | Guoqiang Tang, Tsinghua University
- May 3, 2019: Multioutput neural networks for estimating flow-duration curves in ungaged catchments | Scott Worland, Cornell University and USGS
- May 10, 2019: Remote sensing precipitation using artificial neural networks and machine learning methods | Kuolin Hsu, University of California, Irvine
See also this special issue on Water
or the real numbers 
. Indeed, the use of number systems is so closely identified with the practice of mathematics that one sometimes forgets that it is possible to do mathematics without explicit reference to any concept of number. For instance, the ancient Greeks were able to prove many theorems in Euclidean geometry, well before the development of 
if one wishes to measure a vector-valued physical quantity such as velocity). Much as analytic geometry allows one to use the laws of algebra and trigonometry to calculate and prove theorems in geometry, the identification of physical quantities with numbers allows one to express physical laws and relationships (such as Einstein’s famous mass-energy equivalence 
) as algebraic (or differential) equations, which can then be solved and otherwise manipulated through the extensive mathematical toolbox that has been developed over the centuries to deal with such equations.
; instead, one has to say something like “the length of this object is 
feet; if one instead uses metres, the length is now 
metres; and so forth. But nothing physical has changed when performing this change of units, and these lengths are considered all equal to each other:
seconds, then its speed should be
and 
for metres and seconds respectively. Similarly, if the speed of light 
is 
and an object has mass 
, then Einstein’s mass-energy equivalence 
are being manipulated algebraically as if they were mathematical variables such as 
and 
. By collecting all these units together, we see that every physical quantity gets assigned a unit of a certain dimension: for instance, we see here that the energy 
of an object can be given the unit of 
(more commonly known as a 
where 
are the dimensions of mass, length, and time respectively.
) and 
is a speed (having the units 
), then it is physically “legitimate” to form an expression such as 
, but not an expression such as 
or 
; in a similar spirit, statements such as 
or 
are physically meaningless. This combines well with the mathematical distinction between vector, scalar, and matrix quantities, which among other things prohibits one from adding together two such quantities if their vector or matrix type are different (e.g. one cannot add a scalar to a vector, or a vector to a matrix), and also places limitations on when two such quantities can be multiplied together. A related limitation, which is not always made explicit in physics texts, is that transcendental mathematical functions such as 
or 
should only be applied to arguments that are dimensionless; thus, for instance, if 
is not physically meaningful, but 
is (this particular quantity is known as the 
of a symplectic manifold can be usefully thought of as a component of the exponential 
of the symplectic form 
).
for some dimensionless absolute constant 
; the only remaining task is then to work out the constant of proportionality 
theorem