[MUSIC] Hello. So welcome to this class on simulation and modeling of natural processes. So for this first week, I would like to discuss some introductory material. And present some basic concept. But before I like also to present you the team that will teach you the different chapter. So we have Dr. Jonas Latt. Dr. Jean-Luc Falcone, Dr. Orestis Malaspinas, and myself, Professor Bastien Chopard. So we are all working at the computer science department at the University of Geneva, and we've been involved in the modeling and simulation of complex system and natural phenomenon for many years. Our background is physics for Jonas, and myself and biology for Jean-Luc. So we're an interdisciplinary team, and I think it's one of the key words for this class. So let me start also by explaining to you what we want to do in this class. So, of course, the main topic is to learn how to model natural phenomena. So we will overview several modeling techniques. And we'll try to be theoretical and practical in this approach. So, somehow, once you have a model, you need to run it. Some people call that in-silico experiment or numerical experiment. That's a second important part that we want to discuss. We will learn also how to actually implement this model I mean, simulate some simple case, because, of course, we don't have time to go to very advanced applications. And of course, one goal we would like, also, to reach is that you learn more about science through this modeling approach. I mean, modeling is basically understanding how things work, and it's a good opportunity to understand and to learn more about nature around us. So we're talking about natural processes, what are they? Actually, we have a pretty long list, and I don't think we have so limitation in the type of problem we would like to address. Just here have a list to give you some ideas. So certainly Physics is interesting, like fluid mechanics. We will hear quite a lot on fluid mechanics, but also astrophysics and chemistry. Climatology, maybe we will not touch this problem here, but they are also important domain for modeling. Of course, environmental science, river modeling, volcano modeling, well see some image soon. In biology we can of course be interested in the pattern on animal skin, the cells, how they work, the organs and all this we can try to model all this. That is an aspect. We can also see how tissue grows, I will show you this. The effects of an ecosystem is also very important. You can try to study the competition between species. We can also see how ecosystems, how in competition like different type of vegetation, forest and savannah, for instance. Propagation of epidemia, so there are plenty of interesting questions which are related to ecosystem. But we can also go beyond the standard, I would say, national science to, for instance, a finance economical system, social science. Just to mention example of traffic, road traffic, which is of course an important question for daily life, pedestrian motion. And I'm sure I'm forgetting many other example. So I wanted to illustrate a little bit, some examples. So let me show you an example of a simulation. You will learn about the technique, but not of course to the level of doing such a simulation, but this is an example of a droplet that pops out of free surface. And I was mentioning the modeling of river flow, so it can be interesting for understanding sedimentation process, ecological aspect of a river. So you wanna be able to manage some waterways. And so simulation can be of importance. Okay, sorry for that. We're also interested to simulate volcanoes, in the sense the we have the prum, which is an interesting hydrodynamical process. We also want to simulate the transport of ashes and see what's the danger for the population around a volcano. And, this is a simple example here, of growth of tissues, so it's an epithelium. Where you see cells growing, dividing, and propagating in the space. So this is just an example of a simulation that could be interested to do. We'll try to explain you what other underlying models, what's the idea that we put in the computer to reach this type of system. So a central question of course is what is a model, because that's the main topic of our question. So here you have an image and I guess you all recognize an apple. But what's weird is that it's written in French. This is not an apple, okay? And of course you say, why not an apple? It looks very much like an apple, but actually it's not an apple, because an apple is this. This is a real apple. This one I can eat, I can cut it, I can open it, and everything. The one you see on the screen, you can just look at it, you cannot eat that. It's just the representation of an apple, right. It's not a real apple. Of course it depends what you want to do, if you want to explain someone doesn't know what is an apple, just to give an idea. So this image is perfect. But if you would like this person to test it, of course this is not sufficient. So we see that a model is of course, just, you know, a representation of reality, or at least a representation of a part of reality, but not the full system. So if you look for the definition of model, you will see many things, and depending on the community you will see very different understanding of the word. For some people a model is just a fit of data. For biologists, it might be just an animal on which the experiments, processes, or drugs. So for us, it's really related to representing nature, and I would say it's what I would call simplified abstraction of the reality, in order to better describe it. I think the key point is in this abstraction, you should be able to put important ingredients, important concept. Then this of course will depend on what's the question you have. Again back to the apple, if you choose to show people how an apple looks like. I guess the painting is fine, but it doesn't answer all the questions. We should always know what the other questions we want to answer, and then what are the important ingredients for that. And then, a representation of phenomena through a mathematical description or a computer based abstraction. And of course in that course we will pay very much attention to computer-based models. Actually, if you want to know where this domain fits in Computation of science is an emerging new field, which is actually the place where we should put the society of modeling and simulation. As you may understand to do the modeling and simulation, we need many skill. We have to build the model, we have to run it, we have to analyze its results. So it is a lot of competencies that you should get. And computational science is an emerging field, which is essentially multi-disciplinary. Try to group many knowledge and skill from different discipline. And it put the computer at the center of the methodology. So the idea is that we should try to describe nature using the potential of a computer, not only just to solve, to compute or to calculate things. But to represent nature within a computer language. So actually I computed there for you new ways to describe nature which goes beyond the traditional techniques. For instance beyond equation or things that you may have seen in your previous studies. So the fact that you describe an object or situation in a computer with rules or programs, allows you to address problem far beyond what we can do classically, and that's what we want now. Modeling and simulation is typical example of this methodology. And it came really as a very important aspect, because we have more and more data from experimental observation. But we also want to address more and more challenging question. And also, we don't want to only specialize in a very narrow field, but we want also to integrate, to combine everything we know in a global application. You know that nature doesn't really care about the separation in discipline that the human did, so for nature, there is no something like biology that's separate from physics or from science. Everything is together. And of course if you want to model nature, you have to integrate all these. So I would say that computational science is at the intersection between several field like computer science, like mathematics, like physics. For the domain in which you wanna work. So for instance, biology or economy, so when you're a computational scientist, you probably need to be a bit of all these, and that's not always easy. So I hope that this class will give you at least a way to enter into this field. So with this, I would like to finish the first module about the objective and the background of this course. And the next module will be more dedicated on more specialist aspect of modeling and simulation. Thank you for your attention. [MUSIC]
