ESI BLOG ARTICLESCIENCE TALK
A walk around the ESI arts of modelling, simulation anddecision-making
In a world where technology and science intersect, ESI Group is redefining thelandscape of engineering practices. Prof. Francisco Chinesta, ESI’s ScientificDirector, unveils his groundbreaking approach for modern development engineers
to surpass the limits of modeling, simulation, and decision-making. By following
his innovative formula—selecting the most advanced physics-based model,
gathering the most relevant smart data, merging the two to closely replicate
reality, and utilizing this hybrid model to make precise predictions rapidly and atscale—engineers can achieve unprecedented accuracy and efficiency.

FIRST ACT: Prelude
At the beginning was science. Modern science, as we know it today, was still some timeaway. From the very start, humans created technology to master and benefit from thenatural environment. As time passed, the desire to surpass the status quo and breaklimits led to the birth of engineering. Leonardo da Vinci's dream of flying exemplifies thisdrive—his wings were simply an inevitable means to an end.
As Theodore Von Karman said, "Scientists study the world as it is; engineers create theworld that never has been." The first industrial revolution, powered by steam,transformed industry. Later, electricity sparked the second revolution, enabling fasterand more efficient production, which transformed both industry and society.
About two centuries ago, Alessandro Volta was invited to the French Academy ofSciences in Paris. He demonstrated the effect of applying electrodes to a frog, causing it
to jump, much to the audience's amazement. Napoleon then asked Volta if electricity
could ever serve a purpose beyond making frogs jump. Today, it's impossible to imagine
a single day of our lives without electricity. Later, electronics and automation ushered in
the third industrial revolution, allowing not only faster but better production. Despite
these advances, engineering remained product-based. Society sought performance, andproducts were merely a means to that end. When we buy a drill, we're essentially trying
to buy a good-quality hole. Yet, engineering remained product-oriented. Why?
The limitation lay in the difficulty of addressing products within their complex, uncertain,and fluctuating environments using physics-based models. Additionally, solving thesemodels in real-time for optimal decision-making in design or operation was challenging.
How did ESI address these challenges throughout its accomplished history?

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SECOND ACT: ESI ARTS
ESI tackled these challenges by mastering three major arts:
1. The ESI Art of Modeling: This art encompasses all physics across all scales ofdescription related to materials, processes, and structures. It provided a rich andholistic physics-based approach, enabling ESI already back in 1985 to mastermaterials, manufacturing processes, and structural analysis, even under extreme
conditions with computational resources far less powerful than today'ssmartphones. Despite these limitations, ESI simulated a crash test involving
complex dynamics, multi-contact, plastic deformation, damage, and rupture.

Image 1: The first ever crash simulation conducted with ESI together with Volkswagen
in 1985
The ESI art of modeling was capitalized in its numerous products that enable theexploration of materials involved in manufacturing processes, as well as thevirtual analysis of structural performance. However, the potential
accomplishments were limited by the available computational resources and thecomputational time required for calculations and responses. Transitioning fromproduct management to performance management requires faster predictions
to facilitate quicker and more efficient designs and decisions.
2. The ESI Art of Simulation: To address the limitations of computational
resources and the need for faster predictions, ESI enriched its modeling with theart of simulation. This involved applying the best numerical techniques to eachproblem, ensuring accuracy and efficiency. The need for real-time, or faster,
simulations led to the development and deployment of advanced Model OrderReduction (MOR) techniques, particularly the Proper Generalized Decomposition(PGD). This enabled creating computational vademecums1 that provided

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technical references2

responses to various conditions in almost real-time, embodied in the ESI AdMoReplatform.
The ESI Art of Simulation allows addressing sophisticated physics (stemming fromthe Art of Modeling) within their environments, leading to the ESI concept of the“Augmented Virtual Prototype” (AVP), a virtual replica of a real system.
With an operational AVP, one might think the story ends here, but as Winston
Churchill said, "This is not the beginning of the end, but perhaps the end of thebeginning." AVP enables rich descriptions of physical systems in almost real-time,
but are these descriptions and associated predictions consistent with real-world
observations? In some cases, they are, but in many others, significant deviations
arise and grow over time, limiting predictive capabilities. An epistemic ignorance
seems to persist in our conceptualization and description of physical reality.
Models are models, but sometimes reality contains elements that our models
overlook—this is the so-called “ignorance” (the part of reality our models ignore).

The greatest obstacle to discovery is not ignorance.
It is the illusion of knowledge.
Stephen Hawkins

3. The ESI Art of Decision-Making: As models sometimes deviated from real-world
observations, a new protagonist emerged: the art of decision-making, central to
the fourth industrial revolution. At it’s core is the question: How do we realizethat a model is not accurate enough? By collecting data and comparing thesemeasurements with the predictions based on existing knowledge and state-ofthe-art models. In today's fully interconnected global world, facilitated by theInternet of Things (IoT), collecting abundant data is often possible and relatively
easy.
When these measurements, even when adequately assimilated into the models(model calibration), fail to represent observed reality accurately, it indicates a gap
in our representation of reality that urgently needs addressing to improve
designs and decisions. One solution is offered by Artificial Intelligence (AI), which
has become integral to our lives, technology, and social activities since the end of
the second millennium. Machine Learning (ML) can create predictive models fromavailable or collected data and provide predictions in almost real-time.
However, creating models from scratch because existing physics-based models
are not accurate enough is not ideal. Building a model solely based on datarequires a vast amount of data, which in engineering and technology equates to3

high costs. Additionally, data collection often involves ethical considerations,
regulatory compliance, and technical challenges.
At ESI, we advocate for an alliance between the traditional world of knowledge
and physics-based models and the new digital world of data, leveraging
increasingly powerful AI techniques. This alliance is embodied in the ESI HybridTwin, which enriches physics-based models by addressing their intrinsic
ignorance through data that highlights the deviation between predictions andmeasurements.

Image 2: The ingredients to build a Hybrid Twin; © ESI Group 2024
This approach reduces the amount of data needed, constructs models in realtime, and certifies designs and decisions. Even though the explainability of the AI
component can be challenging, the physics-based contribution is wellunderstood, having been verified and validated over the centuries.
Just as data can enrich existing knowledge (physics-based models), existingknowledge and physics-based models can transform the big-data paradigm into asmarter data paradigm by answering three main questions:•••

What data to collect?
When to collect the data?
Where to collect the data?

For example, to estimate the temperature in Paris, you can use twothermometers: one in your room and one on the balcony, taking measurements
at midnight, early morning, and noon. With just two thermometers and threemeasurements, you get a reasonable approximation of the daily temperature
evolution. This decision, based on your knowledge, avoids the impracticality andexpense of using millions of thermometers across Paris. This illustrates the4difference between big data and smart data. Smarter data collection is clearly
better.
Thus, the ESI Hybrid Twin
constitutes a digital replica of amaterial, process, structure,component, system, or system ofsystems.
Image 3: Hybrid Twin Technology
used in a crash simulation to study
weld rupture, © ESI Group 2024
It can replace or substitute the real system to anticipate future responses andaccess its intimate state, while retaining the following features, qualities, andfunctionalities:
•••
•
••••
•••

Accuracy guaranteed by the hybrid approach
Frugality, based on the smart-data paradigm
Holistic coverage of all involved physics and description scales
Real-time responses through advanced model order reduction techniques
Explicability and certifiability
Adaptability
Reliability
Resilience
IoT-informed
Systemic approach to address the system within its environment
Usability

Success stories include collaborations with Dassault aviation and Thales foraircraft Hybrid Twin, EDF EN for wind farm twins, and CNRS@CREATE for theDesCartes programme in Singapore and Paris as well as numerous awards andrecognitions.
Yet, it doesn’t stop here, because the Hybrid Twin (HT) of a component, system,or system of systems can and must interact with humans, who remain theprimary contributors in many areas. This requires imagination, intuition, complex
abstraction and interpretation, qualitative reasoning, and an emotionaldimension. People remember what they learned very differently after two weeks:
10% to 20% of what they heard or read, but 90% of what they did (acted upon) ina real or virtual experience. Thus, we as humans must experience physics asprotagonists, not just as observers.
Data produced by the HT must be contextualized to be useful, interpretable, andexploitable by humans. New human-centric facilities have emerged and5combined with the hybrid paradigm. Among them are immersive virtual reality(VR), augmented reality (AR) in multiphysics evolving settings, and hybrid reality(HR), where humans interact with the virtual world (a digital replica of reality) in
the form of holograms that can be experienced visually or through touch. Theseinteractions can even integrate other sensory experiences such as smell andtaste while recording emotions to better adapt products to each specific user. For
example, adapting a car to the driver, a textbook to the reader, or a cosmetic
cream to the customer.
This human-centric Hybrid Twin is a key player in the emerging ESI Augmented
Intelligence, which does not aim to replace humans but to enrich theircapabilities in facing the ultimate art: the Art of Decision Making. This art goesbeyond simple reptilian pattern recognition like "I'm hungry, I eat," which isnecessary but primitive. We are discussing a more elaborate dimension ofdecision making that utilizes both sides of the brain—the rational and thecreative.
THIRD ACT: EPILOG
We recently experienced the impact of a pandemic everywhere and adapted to it in an
active and constructive manner. Some activities have proven resilient enough, whileothers, such as production machines and supply chains, remain less adaptable.
Without a doubt, a resilient, human-centric world that combines the real and virtual,cold digital data and warmer emotions—all harmoniously integrated—will constitute thenew revolution. This revolution will be more than just industrial; it will have a significant
human and societal dimension. ESI is succeeding in this new framework, sculpting a newworld with major components such as:••••

Hybrid Twin (combining real-time physics and real-time physics-informed AI),
Blockchain,
Quantum computing,Multisensory and emotional human-centric technology.

This rich and complementary ecosystem empowers human creativity and imagination,
enabling progress that is further, better, and faster. This is not a distant dream; it isalready real through the intimate combination of the three ESI arts: the art of modeling,
the art of simulation, and the art of decision making.
ESI is committed to empowering humanism, both technically and societally, withoutventuring into transhumanism. Each step must be taken at its proper time. The world
will emerge from our actions, and everyone should assume their responsibility. ESI takes
an active and constructive role in this globally sculpted new world, and its continuousand responsible innovation is the best proof of its involvement and major contribution.
6AuthorProf. Francisco (Paco) Chinesta, Scientific Director at ESI GroupFrancisco (Paco) Chinesta is full professor at Arts et Métiers Institute of Technology inParis, and ESI Group Chief Scientist. He has a recognized expertise in hybrid modelling(physics-based and data-driven), allying physics, applied mathematics and artificial
intelligence, the main components of the so-called hybrid twin. He received many
scientific awards, he is honorary fellow of the “Institut Universitaire de France”, fellow ofthe Spanish Royal Academy, Honoris Causa Doctorate from the University of Zaragoza,
Silver Medal of the French CNRS, French academic palms, French order of merit, ... He is
the Director of the DESCARTES that CNRS develops in its hub at Singapore, on Hybrid
Artificial Intelligence for Decision Making in Critical Urban Systems (5 years, 35 M€, morethan 250 years of cumulated research).

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