[ca] El problema de la predicci´o de la resist'encia dels vaixells ´es un tema cr´ıtic en l'arquitectura naval i el disseny de vaixells. Implica estimar la quantitat de resist'encia que trobar'a un vaixell quan es mou per l'aigua en diverses condicions. Aquesta predicci´o ´es crucial perqu'e permet als dissenyadors i enginyers de vaixells determinar els requisits d'energia, l'efici'encia del combustible i altres caracter´ıstiques. Computational Fluid Dynamics (CFD) ´es la t'ecnica de predicci´o m´es fiable en el disseny i l'an'alisi de la forma del casc per a vaixells nous. S'utilitza per realitzar an'alisis de flux de fluids per ajudar els dissenyadors a extreure el m'axim rendiment possible en gaireb´e tots els aspectes funcionals d'un vaixell naval, inclosa la resist'encia del vaixell. Els m'etodes CFD moderns s´on certament capa¸cos d'oferir resultats altament precisos en aquest domini, per'o es basen fonamentalment en m'etodes num'erics que resolen les equacions relatives de conservaci´o de massa, energia i moment, donant lloc a procediments matem'atics intensius en CPU i aix'o dificulta. el seu ´us convenient i la seva integraci´o dins del bucle de disseny de vaixells convencional. Machine Learning (ML) s'est'a convertint r'apidament en una tecnologia b'asica per a la inform'atica cient´ıfica i el seu ´us a CFD ´es un tema de recerca ampli. En aquest treball, proposem models substituts basats en dades, basats en algorismes ML d'´ultima generaci´o, per accelerar l'exploraci´o de diverses formes de casc en una fase inicial de disseny. Els resultats van mostrar que els enfocaments emprats poden ser una soluci´o valuosa i m´es eficient per a l'an'alisi qualitativa inicial de diverses formes de casc per tal de trobar la m´es eficient per a cada 'ambit d'´us

[en] The problem of naval ship resistance prediction is a critical issue in naval architecture and ship design. It involves estimating the amount of resistance a ship will encounter as it moves through the water under various conditions. This prediction is crucial because it allows ship designers and engineers to determine the power requirements, fuel efficiency, and other characteristics. Computational Fluid Dynamics (CFD) is the most reliedupon prediction technique in hull-form design and analysis for new vessels. It is used to perform fluid flow analyses to help designers extract the highest possible performance in nearly every functional aspect of a naval vessel, including ship resistance. Modern CFD methods are certainly capable of delivering highly accurate results in this domain, but they are fundamentally based on numerical methods that solve the relative equations of mass, energy, and momentum conservation resulting in CPU-intensive mathematical procedures and this hinders their convenient use and integration within the conventional ship design loop. Machine Learning (ML) is rapidly becoming a core technology for scientific computing and its use in CFD is a wide research topic. In this work, we propose data-driven surrogate models, based on stateof-the-art ML algorithms, to speed up the exploration of several hull forms at an early design stage. Results showed that the employed approaches can be a valuable and more efficient solution for the initial qualitative analysis of several hull shapes in order to find the most efficient one for every scope of use.