Short-bio: Ph.D. in Mathematics (1975) from Carnegie Mellon University. Recipient of national and international awards. Interested in teaching and research. Passionate about science—particularly mechanics in its various forms—and especially about deterministic and stochastic modeling. He has taught at both the high school and university levels, and some of the students he mentored have achieved success in academia. He has actively fostered the development of science in Brazil and promoted international cooperation. An active member of ABCM and SBMAC.

Prof. Sampaio is a PUC-Rio Emeritus Professor of Mechanics, a former President of the Brazilian Society for Applied and Computational Mathematics (SBMAC), former Editor of the Journal of Brazilian Association of Mechanical Sciences (ABCM) and of Notas de Matematica Aplicada of SBMAC. Prof. Sampaio main interests are Continuum Mechanics, Dinamics, Stochastic Mechanics, Scientific Computation, and Applied Mathematics. Prof. Sampaio is an Alexander von Humboldt Fellow, a Chevalier d’Ordre des Palmès Académique de la République Française, a Comendador da Ordem Nacional de Mérito Científico do Brazil. He has cooperated for more than 40 years with the USA, France, Germany, Portugal, Spain, Chile, Argentine. He is Pesquisador 1A of CNPq, Cientista do Nosso Estado da Faperj for the last 15 years. His main achievement however are the students he helped to train: 28 doctors, 41 masters, and several pos-docs. He is the Secretary of the ABCM-Committee of Stochastic Modeling and Uncertainty Quantification.


Keynot Lecure

Title: A Happy Love Triangle: Thermodynamics, Newtonian Mechanics, and Randomness.

Thermodynamics is a fairy tale! Time and space do not count; processes are quasi-static—meaning they are not real processes. Newtonian mechanics is the paradigm of determinism, a worldview of a well-ordered system functioning perfectly, not only in the present but also in the past and future. The world as a model governed by an equation! Randomness is the opposite of determinism; uncertainty reigns! It resembles life: from nothing to something and back to nothing, with plenty of fun in the intervals between the “nothings.”; How can such disparate concepts come together?

The Industrial Revolution, with the development of heat engines, gave rise to the concept of entropy—even before the establishment of energy conservation
or the acceptance of atomic reality. In parallel, Newtonian ideas—and their success in describing celestial mechanics—led to a new branch of physics: statistical mechanics. This new perspective fostered the development of a different concept of entropy based on statistical mechanics and information theory, an idea pioneered by Shannon. How are these entropies related? One is deterministic, originating from heat engines, while the other is random, associated with information. This lecture aims to explore the relationship between these ideas from a historical perspective, with a particular emphasis on the introduction of the concept of randomness into physics.

Short Bio: Alexandre A. Emerick, Petrobras Research, Development, and Innovation Center Senior Advisor at the Petrobras Research Center (CENPES) in Rio de Janeiro, with over 20 years of experience in applied research in reservoir engineering. His expertise includes reservoir simulation, data assimilation, uncertainty quantification, and optimization. Dr. Emerick has extensive experience in research, software development, training, and the application of ensemble data assimilation to petroleum reservoirs. He has authored numerous peer-reviewed papers on data assimilation and holds a Ph.D. in Petroleum Engineering from The University of Tulsa.
Keynote Lecture

Ensemble data assimilation applied to geological reservoir models

Abstract: Geological reservoirs are complex subsurface systems characterized by strong spatial heterogeneity and limited direct observations. Their description relies on geological interpretations, geophysical measurements, and dynamic production data, all of which are affected by significant uncertainty. As a result, reliable reservoir forecasting and decision-making require methodologies capable of integrating diverse sources of information while consistently quantifying uncertainty.

 Reliable forecasting and uncertainty quantification in geological reservoir modeling require the consistent integration of prior geological knowledge with dynamic observations. This presentation provides an overview of ensemble-based data assimilation methods for reservoir characterization and prediction, with emphasis on their Bayesian foundations and practical implementation. Special attention is given to iterative ensemble smoothers, particularly the Ensemble Smoother with Multiple Data Assimilation (ES-MDA), which has become a powerful and flexible methodology for data assimilation and uncertainty reduction in real reservoir applications.

 The talk will discuss how ensemble data assimilation enables the updating of geological reservoir models while preserving geological realism and quantifying posterior uncertainty. Both theoretical foundations and key practical aspects will be addressed. Examples from synthetic and field-inspired reservoir studies will illustrate the connection between theory, algorithms, and decision-oriented workflows.

Although motivated by petroleum reservoir applications, the concepts are broadly relevant to subsurface systems involving uncertainty, including groundwater management, carbon capture and storage, and geothermal energy.

Dr Alice Cicirello is a University Assistant Professor in Applied Mechanics at the Cambridge University Engineering Department and a Fellow at Churchill College. She is the founder and head of the Data, Vibration and Uncertainty group and Executive board member of the European Association of Structural Dynamics. Alice was the chair of the first three editions of the workshops on Physics-enhancing Machine Learning (PEML) in Applied Mechanics, and of the first 3-day PEML event in 2025 at the Headquarters of the Institute of Physics.

Alice obtained her PhD from the University of Cambridge in 2013. She was a Marie Curie Early Stage Researcher (2009-2012) and a Research Associate (2012-2014) at the same institution. Alice worked as a Senior Research Scientist at SLB (2014-2017) and returned to academia as a Lecturer at the University of Oxford (Engineering Science Department and Balliol College, 2017-2019), and then continued as an Associate Professor and Section Head at TU Delft (2020-2023). Alice was also an Alexander von Humboldt Experienced Research Fellow (2023- 25).

Alice is serving in the Editorial boards of Data-Centric Engineering, Machine Learning: Engineering, Advances in Engineering Software, Machine Learning: Engineering, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, and ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, and has served in the scientific and organising committees of several international workshops and conferences. Alice held visiting positions at several research institutions, including MIT, the Alan Turing Institute, and the University of Oxford.