Computational Mathematics & Scientific Computing Researcher

Specialising in numerical methods, adjoint-based optimisation, and scientific computing to solve inverse problems and high-fidelity simulations across geophysics and fluid dynamics.

Adjoint optimisation
Finite element modelling
Wave propagation
Open-source software

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About Me

I am a Research Associate at Imperial College London, working on the Firedrake project. I hold a PhD in Science from the University of São Paulo, Brazil.

My work involves developing and applying differentiation algorithms with Pyadjoint and Firedrake across the field of continuum mechanics to simulate a wide range of physical phenomena. My expertise lies in computational mathematics applied to computational fluid dynamics, inverse problems, and optimisation.

I am proficient in Python programming and have contributed to several open-source software projects including PETSc4py, Firedrake, FEniCS Project, and Pyadjoint. My research focuses on developing numerical methods for wave propagation, full waveform inversion, and fluid dynamics problems, leveraging high-order finite element methods and automatic differentiation for gradient-based optimisation.

Let’s connect

Email: daia.dolci@gmail.com
Imperial: profiles.imperial.ac.uk/d.dolci
LinkedIn: linkedin.com/in/id-dolci
GitHub: github.com/ig-dolci

Current focus

Automatic differentiation Automated gradient for PDE-constrained optimisation problems
Geophysics Full waveform inversion & uncertainty quantification
High-order numerics Robust finite element solvers for complex geometries

“Nothing in life is to be feared, it is only to be understood.”

— Marie Skłodowska Curie

Research Interests

Full Waveform Inversion

Developing efficient algorithms for seismic imaging and parameter estimation

Adjoint Methods

Implementing gradient-based optimisation using automatic differentiation

Finite Element Analysis

High-order methods for acoustic and elastic wave propagation

Computational Fluid Dynamics

Navier-Stokes simulations and flow optimisation

Teaching

My portuguese classes

You can access my teaching materials and resources here.

View Materials

Publications

Journal Papers

An adjoint-based methodology for sensitivity analysis of time-periodic flows with reduced time integration

Tomás Sambiase Privato, João de Sá Brasil Lima, Daiane Iglesia Dolci, Bruno Souza Carmo, Marcelo Tanaka Hayashi, Ernani Vitillo Volpe

International Journal for Numerical Methods in Engineering, 2025

Velocity model-based adapted meshes using optimal transport

Thiago Dias dos Santos, Alexandre Olender, Daiane Iglesia Dolci, Bruno Souza Carmo

Geophysics, 2024

checkpoint_schedules: schedules for incremental checkpointing of adjoint simulations

Daiane I. Dolci, James R. Maddison, David A. Ham, Guillaume Pallez, Julien Herrmann

The Journal of Open Source Software, 2024

Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion

Daiane Iglesia Dolci, Felipe A.G. Silva, Pedro S. Peixoto, Ernani V. Volpe

Geoscientific Model Development Discussions, 2022

Sensitivity of the least stable modes to passive control for a flow around an elastically mounted circular cylinder

Daiane Iglesia Dolci, Bruno Souza Carmo

Physical Review Fluids, 2022

Adjoint sensitivity analysis of steady laminar flows with respect to nongeometrical parameters

Daiane I. Dolci, João de Sá Brasil Lima, Tomas S. Privato, Bruno S. Carmo, Ernani V. Volpe

International Journal for Numerical Methods in Engineering, 2021

Bifurcation analysis of the primary instability in the flow around a flexibly mounted circular cylinder

Daiane I. Dolci and Bruno S. Carmo

Journal of Fluid Mechanics, 2019

FWI Performance Improvement with the Use of Wavelet Transforms

S. Malvar, D.I. Dolci, B.S. Carmo

Digital Subsurface Conference in Latin America, 2021

An Approach for Optimizing the Performance of an FWI Application on HPC Clusters

J.F.D. Souza, F.A.G. Da Silva, D.I. Dolci, L.R. Manrique, E.S. Gomi, H. Senger