Ignacio Brevis

Contact

• workRoom A12 School of Mathematical Sciences
  University Park
  Nottingham
  NG7 2RD
  UK
• Ignacio.Brevis@nottingham.ac.uk

Biography

Research Fellow at the "Additive and Intelligent Manufacturing of Multi-functional Membranes" (AIM3) project.

Research Summary

My current research is in the area of numerical analysis of partial differential equations (PDEfs) and scientific machine learning (SciML). The central aim of the research is to develop and analyse… read more

Selected Publications

• BREVIS, IGNACIO, MUGA, IGNACIO and VAN DER ZEE, KRISTOFFER G., 2022. Neural control of discrete weak formulations: Galerkin, least squares & minimal-residual methods with quasi-optimal weights Computer Methods in Applied Mechanics and Engineering. 402, 115716
• EMAN ELSAYED, IGNACIO BREVIS, SATHISH PANDIYAN, RICKY WILDMAN, KRISTOFFER G. VAN DER ZEE and BEGUM TOKAY, 2024. Controlling ZIF-67 film properties in water-based cathodic electrochemical deposition Journal of Solid State Chemistry. 338, 124820
• IGNACIO BREVIS, IGNACIO MUGA and KRISTOFFER G VAN DER ZEE, 2021. A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations Computers and Mathematics with Applications. 95, 186-199
• BREVIS, IGNACIO, RODRIGUEZ-ROZAS, ANGEL, ORTEGA, JAIME H. and PARDO, DAVID, 2019. Source time reversal (STR) method for linear elasticity Computers and Mathematics with Applications. 77(5), 1358-1375

• View all publications

Current Research

My current research is in the area of numerical analysis of partial differential equations (PDEfs) and scientific machine learning (SciML). The central aim of the research is to develop and analyse physics-based deep-learning computational frameworks for chemical processes. The key concept involved is the learning of the free energy underlying the partial differential equations describing reactive mixtures, from sparse data typical in membrane manufacturing.

• I. BREVIS, I. MUGA, D. PARDO, O. RODRIGUEZ and K.G. VAN DER ZEE, 2024. Learning quantities of interest from parametric PDEs: An efficient neural-weighted Minimal Residual approach Computers & Mathematics with Applications. 164, 139-149
• EMAN ELSAYED, IGNACIO BREVIS, SATHISH PANDIYAN, RICKY WILDMAN, KRISTOFFER G. VAN DER ZEE and BEGUM TOKAY, 2024. Controlling ZIF-67 film properties in water-based cathodic electrochemical deposition Journal of Solid State Chemistry. 338, 124820
• BREVIS, IGNACIO, MUGA, IGNACIO and VAN DER ZEE, KRISTOFFER G., 2022. Neural control of discrete weak formulations: Galerkin, least squares & minimal-residual methods with quasi-optimal weights Computer Methods in Applied Mechanics and Engineering. 402, 115716
• IGNACIO BREVIS, IGNACIO MUGA and KRISTOFFER G VAN DER ZEE, 2021. A machine-learning minimal-residual (ML-MRes) framework for goal-oriented finite element discretizations Computers and Mathematics with Applications. 95, 186-199
• BREVIS, IGNACIO, RODRIGUEZ-ROZAS, ANGEL, ORTEGA, JAIME H. and PARDO, DAVID, 2019. Source time reversal (STR) method for linear elasticity Computers and Mathematics with Applications. 77(5), 1358-1375
• BREVIS, RODRIGO I., ORTEGA, JAIME H. and PARDO, DAVID, 2017. A source time reversal method for seismicity induced by mining INVERSE PROBLEMS AND IMAGING. 11(1), 25-45