Presenting complex data. Uh, that can be consumed by different visualization tools.
So, what we do is we look range of processing apps to kind of.
Convert the data through, I think, a really fine. Format part is often USC, which also acts as a single source of truth.
And that we work closely with, um, stakeholders to come up with different experiences, and these experiences range from.
Inversely visualization, like, here we are. I'll show you some. Are the, uh, complex utilization of the account, essentially.
Required all the individual community for communications. And physical engineering workflows that involve, like, robotics or manufacturing power, consuming that data into essential decision making.
So, this can be an immersion. Um, so, um… to support, uh, the, uh, ecosystem that I explained.
Uh, we got… as well as OBX platform, which is a new cluster that has, uh, that's undergoing its swathom at the moment.
And that is aimed as something… doing all this together. This particular platform is, uh.
A medium-type semester. Uh, having about 64 Gmail and 16 loans, um, and that's only impact visualization.
So, the idea is to utilize the cluster for building these experiences.
Representing complex data. And, uh, helping us in delivering these experiences to.
Devices like laptops or mobile phones that don't have any good compute originalization power.
Um. So, this is another representation of, um.
Same, uh, of green one? But canon, the example shows how different teams kind of collaborate.
Uh, in bringing, uh, unified visualization. So, for example. Uh, through the research team, uh.
Well, it's simulation and ML modeling, and they generate AI and simulation data.
There's engineering design team that utilizes VLR for delivering CAD models.
And based on a generic design data, and then there's fusion operation teams.
Uh, that do these experimental campaigns, and they generate operational data.
And my team sits after that, which kind of closely works with these teams to understand their data.
And we generate these visualization workflows, and. Or both closer in to come, uh, different kinds of visualization and experience.
Utilization to where we are to collaboratively transport. To interact with user experiences.
And here, whatever, uh… data that's coming up gets converted to a reposition, which can be kind of utilized by the key stakeholders.
And now that, uh, information, which is… Can we use for decision-making, uh, and one set use cases that.
Uh, that have decision-making needs to do. Policies and decisions.
But again, that's just one example. There could be a number of materials involved, for example, someone getting an operational control room.
Would like to consume this information, uh, and do a decision-making to offer it commercially.
So in this talk, I like to show you a couple of, uh.
Use cases, uh, coming from our. The Philippines, like Congra and Mazdao.
Um… But before that, this is, again, another, uh.
Workload, um… of the experience generation.
So, again, do that couple of slides, which, uh, have these broad diagrams, and I'll… quickly jump onto, uh, more visualizations. But, uh, current workflow, uh.
Looks like we work very closely with stakeholders, and we follow the design process.
In which, um, the first part in gathering the requirement. Uh, it's like, what is it that they… Stakeholders are interested in looking at in terms of the complex data.
And then comes the iterative design process. Where we work closely with the stakeholders and the other.
Operators and, uh, owners of the data. To come up with a design and visualization that goes in a creative fashion. So, I mean, it's something… if I can guide something about working value in a different way.
So that process goes on in loop until the final design is kind of.
Approved by the stakeholder. And once it's approved, uh, we kind of deliver the final experience.
So that's, like, one of the service models we've got, uh, in terms of… Do you know, whether he's experience was…
And you can quickly catch that there's a lot of communication that is involved.
And communication itself can act as, uh. New kind of data or knowledge.
So, we are currently working on interviews with co-pilot agents for.
Requirement, gathering, knowledge capture, knowledge transformation, and workflow automation. Such that, uh, the communication that's going on at the moment also gets captured by co-pilots.
And we could reuse our workflows. So there is a project going on at the moment.
But involves, um, co-pilots, I mean. I think as a central communicator.
Uh, and operating part of it, obviously most of the workflows, uh, where possible.
And also, the agents can lead for analytics, feedback, and engagement.
Um, but this is all about, um, visualization service. Uh, so far, the framework has been, uh, applied in different facilities within UK.
Uh, I've got examples from Arsene Carmero, but Liberty is a new… facility that is coming up, and I was told that, uh, you know, people are working in electronics.
So Liberty is a new facility that has an electronic source, and the idea is that.
Then you could be combated to, uh, new materials for discovery.
Um, I tend to even make my composite fusion. Um.
We've got different food types at the moment, uh, for data processing. We are using.
Applications like PowerView, Blender, WearMask, which is our internal tools. Airflow and Galaxy for workflow integration and automation.
Uh, for integration, Open University is, uh, kind of considered as, uh.
The central connector, um… We are utilizing platforms like NVIDIA Omniverse, uh.
And web itself, dashboards for, uh, visualization, per se. Uh, for your co-pilots, currently, uh.
We are exploring different co-pilots that are. They're out there in the market. Uh, but because we are very closely tied to Microsoft, uh.
Might be particularly doing microscopilots. Although we are also trying to use, um, time typically or OpenAI with Copilot.
Uh, for compute, uh, there's a large-scale community optimization cluster that is, uh, currently undergoing installation at.
Decided, but then we have access to, uh, more visualized facilities within UKEA.
And it deals with that, and at the US Action Labs.
So, what's Omniverse, um… Omniverse is, uh… massively, uh, extensible, uh, software.
Uh, which is… right now for 3D design, collaboration, and Simulation.
Uh, the platform itself is very notably defined, uh, where… Uh, there is a central nucleus over, uh, which acts as a central repository for collaboration.
And that's where all the files and data get stored. So, think of it as one prime equivalent.
And this, uh… UPS kind of links to the Omnibus-specific applications that, uh, can…
Be something which comes with Omniverse, like. Paid for, uh, I mean, visualize very complex scenes.
Or it's also possible to build custom applications using extension tools, uh, data-labbing technology.
So the platform itself is very, very, uh, loosely designed. Uh, and there is, uh… lot of scope out within the ecosystem, which are currently working.
Um. So, building NPS as a central server, it is possible to.
Pushing more sources of data that are… that can be, um, controverseneous in nature.
Uh, but it's also possible to pull in data from Nucleus, which is, like, a single source, uh.
Or open USB, and… It's possible to generate data visualization that varies from photorealistic visualizations.
Rendering, complex handling VFX workflows, uh. Or ARVR, uh, things such as, uh, using, uh, Apple Vision or any sort of 3D glasses.
For data exploration. So this is one such example of, uh, Chimera digital experience.
Chimera is a facility for, uh, material testing. Under freehand condition, and the idea is to test material under.
Extreme thermal mechanical as electromagnetic stresses. Is kind of supported by simulation, uh, and it generates, like, very large magnificent simulation data.
That can inform terabytes to petabyte. And the workflow that you can see on the right.
Uses USDA as a central source, uh, where. The simulation data goes through a range of data processing steps.
Uh, and the data gets pushed to USB. Goes through.
And that can push to. Ufe. Um, and here, the USE itself is, like, uh.
Three, kind of, upon, like, where you can kind of merge different sources of data.
And a non-destructive fashion. I'd love here is a central data source present.
It's possible to kind of ingest their data, do data integration, interactive exploration, collaboration.
Um, so the set output looks like this, uh… Where the CAP data is kind of, uh, superimposed with the.
Simulation data, and then that used to, like, these four different kinds of visualizations.
Showing different quantities, so the one that is closest to you in the front.
These are photos visualization of the camera block, um. Which takes the, uh, properties of.
Part-time lows. Uh, so that's the visualization of how the actual property looks like.
Well, the remaining 3 are, uh… do representation of… how stress or changes the property of the material.
What happens when it comes to, like, providing peace or condition?
And, uh, the last one is, uh, material displacement, which kind of deforms the material.
And using the same source of data, it's possible to generate more.
Engaging visualization that can be kind of general public. So, what I showed in my previous slide.
The same data set is, like, now consumed by a blender to generate.
Uh, this video. Looks like this. You are again looking at temperature and stress and, uh, the time period on how the data looks like.
Um, and this is a bit the similar visualization, where, uh.
We only visualize the data rights of a single timestamp, which what you're seeing is, uh.
A great day of fluid velocity. It will be kind of meta-blocked.
Uh, and this is, like, one of the largest known thermon hydraulic simulations.
We've got a boot on this particular facility. Using the, uh, frontier HPC.
Uh, and then it took us quite a lot of effort to not just do the simulation.
But to bring the data from U.S. National facilities like Frontier.
To the UK, and then when we process it, that they're on… Uh, then more difficult to come to Cambridge, which is where the visualization service setting.
Um, and then we are able to generate this data.
Um, this is another one, um… Uh, which is the MOPS view. You'll come back to the virtual experience, and, uh.
What you're seeing is a combination of engineering cap data and assimilation data.
So, uh, the… structure of the document, which is, like, uh.
A cut-down version of the actual facility, which resides within UKA.
Uh, the plasma is coming from a joint, which is MSV code for generating plasma simulations, and… That's until the end. Um, and it uses a similar process of integrating CAD materials and simulation data.
To create a combined credibility to it. And once you've had the vision of doing it, what's possible to VR walkthroughs.
Collaborative design, decision-making, and it's also possible that. I think sort of changes can be quickly seen. So, for example.
The simulation that you are seeing, uh, it's using. But if the generic team decides to change the mesh, uh.
Possible to see if there will be any changes, or whether those changes can come and get it back to the.
Other teams that rely on, uh, linear data.
So, uh, this one is, uh, one example, which I will explain it.
That uses, uh, circulation data, essentially concentrating about 1,000 BTK pies.
Processes it and combines it. The FBX of the CAD models, uh, of the facility.
Uh, within, uh, and with the omniverse. Uh, and therefore, we kind of assign the real materials, realistic materials to these sources, to generate, uh, visualization that.
Looks like this, which is… real estate and nature, because it's impossible to generate devices like this, uh, which can be used for public and outreach.
Uh, we also took a step to generate, uh. Time series animation of…
Our data would… an experimentalist who is, kind of.
They end up looking at camera data. So, what we did was we took the parameters of the actual camera, which is the same.
Last year, uh, we modeled the virtual version of the camera and rendered, uh, the simulations.
In the way that they appear very similar to the rear camera.
So, uh, experiment for these two are for the first-line workers who are.
Where he used to, uh, seeing the real data that comes from the real camera.
And it informed decision, they look at the simulation data in a similar way.
And this is all the possible, uh… doing just a single source, um, so again, we are not…
We are not generating large number of files, we are just… ingesting the data from the Central OpenUFC.
And making progress related. Um, this is another example of…
What you're seeing on your left is, uh, scientific visualization of.
Duty and free cream retention, uh, within the diverted block of CPOCMAT.
Essentially, the local constant. Uh, but on your right, what you see is…
When the same dog gets irradiated. Uh, you see these facets that we do over time.
And this is, again, a visualization which is aimed for general public.
That shows, um, how the stress is filled up over the time.
And I would slide in terms of equivalent time. Which is what accidental start used to say.
Um, this one, uh, is again an interesting example of immersive experience, where.
Uh, we took the CAM model of mass steel, uh, and combined the.
Simulation data, which comes from, uh, joint simulation. To create a premium model that looks like this.
And, uh, we kind of… created a habit for visualizing this very fabric.
So, uh… It's, uh, anyone who has, uh.
Smartphone, and if they, uh, kind of scan this QR code.
It would take you to our 3D viewer, uh, viewer, where they can kind of explore this.
We want, uh, the mobile phone. Um, but if they press the button, which is viewed here, they can also visualize the same module.
Just, like, it's got me… quite engaging for, uh.
And it kind of… brings in, uh, discovery and insight, as we know.
Um, we are also working on, uh, using this technology for building adaptive dashboards.
The fact that, uh, we can kind of, uh. Combined resources of the data and bring them to the control room operators.
There was a talk that I delivered, uh, early in the morning, uh, in a conference.
That talks about, kind of. Sources of data, uh, and helped me.
Dashboards using the area. Um, this one is, uh…
A volumetric 3D representation of the data. So, we've got a… do we monitor its site that, um.
Essentially, uh, shows you the data in 3D without wearing any special glasses or any equipment.
Uh, so this is one example. Shows how a worldwide TV looks like.
Their data will come out from the same.
So, uh, in a nutshell, the program itself is aimed at providing these different experiences.
Uh, that range from, again, uh… immersive experiences, uh, scientific visualization, and the intellectinary workflow.
So, currently, we are… Uh, working on, uh, Omniverse applications for customizations.
So, we are trying to… that incorporates most of our work towards the automation tools.
Um, we are also working with our collateral returns, uh, at University of Manchester.
Uh, and the other departments in bringing in real-time robotics and manufacturing integrated them.
Um, with the integrated engineering team, uh. Here are some work going around interoperability with PLL payment external systems.
Um, essentially bringing in data from different vendors to a unified.
Um, representation. Uh, the AI and simulation model embeddling is also where we have a new.
We have everything coming up with ideas to bring in some sort of target models.
To a salute, uh. The extended nights and simulations, and the visualizations.
There's lots of projects going on, uh, back in this live data stream, uh, can dynamically for the integration.
So with that time, that might come out there, and I'm open to… Thank you, that seems that we have on my slide, so…
In general, uh, we are… collaboration, we are already collaborating with.
From different, uh.
For 8 to move your thoughts.
Questions in the room. Yes, please, Chris. So, I have a question. I mean, I have a friend who worked on JetJet, okay, and uh…
And of course, what he was… it was an experimentalist, and he was able to constantly discovering new.
Instabilities that have not been predicted, and then the theorists would work to try to actually model those in science, because it's going very much from the experimental. Well, you showed on masks, and you're so regular and so on.
And you didn't put the word discovery there, but can you discover.
New phenomena such as instabilities from these simulations, or do they come more.
From the experimental data. Yeah. Yeah, so that's where our, um…
Uh, so the… the fixed range models, um. Because they rely on set of equations, they will…
I think when there are limitations. So that's where our AI-level workflows are coming into picture.
So we use, uh, techniques such as ethanol spins to kind of model these instability.
That, uh, not just rely on the raw experimental data. But also the physics-based modeling, because.
A lot of these instabilities cannot be modeled using physics equations, but.
Combination of the equations and ML. Can kind of bring in modeling, uh, such instability.
We have… there is a multi-month, which I've been covered by myself.
What questions? Yes, sir. Is there any guidance on the accessibility?
We have the data chain. And we have the…
The old model to avoid any potential data leak, or… You know, apart from login passwords.
I will build on the government. Like, how to access that information.
Yeah, so, I mean, that kind of goes… Part of beyond the work that my team does, uh, but within computing.
We have the team that is, uh, looking at the security vulnerabilities, and we keep on testing.
The facilities inside. So there's a lot of guidance, like in the internal UKA.
Um, I'm… not very sure about, uh, the sources. Maybe it's something from government, or…
But then we rely on different, uh, information from several sources.
But we also test our internal systems. But that's, uh, I think that's the difference between it, that kind of looks into it. Although I work quite closely.
But security, uh, and vulnerability, uh, in terms of data. Is one area in which, like, we are kind of working on at the moment.
The reason is that we kind of deal with, uh. Release your data, and the data is deciding at different locations.
So, um, there is a new project called Science BMZ. Uh, but you're coming up, uh, that is aimed at sharing the data in a secure fashion.
Uh, such that people are not restricted in terms of data sharing, but we are also secure.
Um, but that's a new product that's not with digital assets.
Other question? Yes, please, normal. Thank you for your book.
Interesting to see how the fields tackle massive data problems. Two questions in mind. What… what is… what language is used for the…
Simulations. The fundamental simulations, if you're simulating a plasma behavior, is that?
Right? Which is that domain you have to know. Um, yeah, so that depends on the tools and the software that is used by the.
Uh, simulation team. So, for example, as far as I know, IPAR people using Quotron.
It was the best minutes fruit by 10. And that all varies.
Really enough, I thought forward my finger in there. It's probably each.
Yeah, yeah, and there are, uh… some of the new implementations that have.
Uh, kind of in demonstrated these simulations to utilize GPUs. That we could even look after.
The other question was, you talked about 5XD as a framework.
And then Vidia's Omniverse platform. Are these… Does… do you access the NVIDIA platform through the 5XD framework, or…
Of these two bits work together, or have I misunderstood what you're doing? Yeah, the 5HP framework is a conception framework. It's a design framework.
That aims at capturing the two. Light omniverse. As well as people.
Uh, the team that we work with. And the processes that we follow.
Is it software, ultimately? But the framework is, no, it's a design framework.
So, climate change between the processes. But the only offer that we use. Okay, so Omniverse is the software, and 5XD is the thing that.
Creates the flow diagram, sorry. Is that the building dog? So, for example, if any newcomer wants to.
Start printing these experiences. Like, these are the blanks and spots we will have to fill in.
In order to generate these experiences. Where, uh, Omniverse acts as an enabler.
Um, and really. So, for example, we are utilizing Omniverse. But another organization can, you know, like, for example.
Uh, there could be other tools, uh, that might be more relevant to a different organization. What does it use at the moment?
Uh, I'm not too sure, because ITER is still going through the design and customization. They must have simulations and visualizations.
They had nothing as part of this problem. Yeah, so I… what I didn't show was actually a simulation that was, uh, done by, uh, one of the groups, uh, within…
The computing, uh, that takes the, uh, the water short, uh, the time there are kind of simulations between moves and neck, uh, necklace.
But they have taken the block from ETA. Well, I mean, we are working closely, but, uh, we…
Are essentially not, uh… the ones that are building these tools for.
You can. Um… Oh, sure. And again, there are a range of challenges.
Which, uh, it's like… We all got reconnect.
And so, answer is that I'm not too sure what they're doing.
But again, we are working closely in different places. Okay, thank you.
More questions, Tim? Just following on from the… Questions about, um, omnibus, um, the worry with using.
Software from NVIDIA is that you get locked into there. Their platform and their system, uh…
What… how dependent are you on that particular. Software. If they… some of these people are going to charge you lots of money.
You should start using it. I mean, uh… What they'll do is they are building these hybrid models where, uh.
Some of the tools and technologies that are out normally works are being open source, uh.
I've only got per se. Is the lightest model. And we have kind of a flag license to.
Creating tools and technology, um… But the central selling templates were only worse, which is hard to, uh, I mean, find out, certainly.
Is representing a massive amount of information. A visualizing massive amount of information that can be utilized, uh, or can be utilized using a set of GPUs.
So NVIDIA, personally, is, like, the leader in terms of GPUs when it comes to visualization.
And the software that, uh, we delay the harness is the power of these CPUs for.
Representing parts of data sets. Uh, so it seems to be working well for us.
And, uh, we are kind of operating in, uh. Hi, good morning, where bits and pieces are open source, bits and pieces can be developed.
And some part of it is kind of licensed. Um, but again, the…
Get logged in. To vehicles, but so does the, uh.
Kt is one tool which is used by the. And by having designed facility.
Various, like, hard to find replacement. Uh, so in serum fashion, uh.
And we tell you, but somehow it is kind of working for us, but we are also exploring.
More ways to, kind of, build these experiences. Uh, that not just utilize.
Omnibus, per se, but we kind of take data from OpenUSP, which is an open format that is not owned by ND.
And then again, the technology step, we can understand that, and we do it on top of a community.
That's what other vendors are doing. When it comes to data visualization, but again, we are a small team, so we are…
Put this on using my experiment.
Are there more questions from the room? I have a question regarding the simulation. I noticed that the simulation data seemed to be on several orders, and a few orders have magnitude larger.
Than the other sources of data. So I was curious what… what is the reason for that, whether it is statistics or the description of the system, so…
Which is the main contributor to this large… data, so, uh, simulation data.
I mean, it's the probability which is important. So, until we, kind of.
Go to high fidelity. Uh, that's the main horizontal requirement of doing these simulations.
Um, so the data policy can be… huge in terms of, uh.
What you see comparing the fixed tab models. I just wanted data.
And that's because the data, per se, is a so-called 3D in nature, uh.
It's not just surfaces or not just to be plots, the detectives are depicting.
And in order to see the minor perturbations, like turbulence, or L.
It has to be kind of simulated at very high resolution.
Um, so be visualizing that I showed were actually. Um, in order of terabytes, but the resolution was 1HG.
In terms of what it should be. Um, at the full revolution.
The, um, order of magnitude. Which kind of… makes it even more challenging to…
If you lies, because then usually. Maybe, like, a memory… Uh, which… and with everything for visualization.
But if they will, in fact, and discoveries with an assumption even agreed by these scientists.
To kind of delivered to students. Yes, and then a disable ways showing data, the products of your.
Framework, which is the end products, or the results that people see or use.
What overhead, or how much… what's… what's… how much data you create from.
The framework. Yeah, so that… That varies from the requirement.
For example, I had one trigger.
Okay. So this is… really, um…
But to give you an example, because there is a block diagram is listed here.
Here we are dealing with, uh. For this example, we are dealing with terabytes of data.
Uh, but the end result is… the filling me.
Now, uh… If the requirement is… a low-edition PhD, the data side will be small.
And our pipeline will use, kind of, uh. A less complex, uh, processing of data.
But the requirement is to generate a very highest emission visualization.
Then we switch gears, and we kind of post-process the data in a very high-resolution fashion.
Which leads to definition of, uh, like, more data. So, uh, in our case, it… really, uh, what the requirements are.
So the requirements are to kind of create images or videos at what presentation is… It's required, like, what is it that they are looking for?
Uh, the event title, and… Um, I mean, the videos can change.
But what we are trying to do is to build, uh.
A system where, uh. These experiences can be streamed onto a browser or onto a machine.
Such that, uh, that bottleneck or challenge for… processing their data on a local machine can be uploaded to something which is.
Sitting, uh, at the level, uh, and then you get to see the results. So you just experience the… we have pre-processing.
Goes on and happened from the cluster side. So that's something we have to actually find.
Thank you. Any further questions on the room, or on Zoom? Are there any questions on Zoom?
Well, and Les, can you speak again?
Uh, we'll be heading