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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. In.

Condizione: New. In.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: new. Hardcover. Polymeric materials play a key role in supporting the ever-increasing demand for electronics, medicines, plastics, sensors, and the transition to renewable energy sources. This is achieved through polymers' distinct features at different structural and temporal scales (i.e., a subtle change in their atomic or mesoscopic structures leads to a totally emergent functionality). However, the design of new polymeric materials is still a lengthy process. This major challenge is related to their inability to comprehensively bridge phenomena that occur at temporal scales from tens of nanoseconds to seconds or spatial scales from nanometers to meters. Indeed, scientific datasets in this field are sparse and include only directly observable quantities, while the underlying processes are either too complex to observe directly or are completely unknown. To move towards an accelerated on-demand design for polymeric materials, recent breakthroughs in scientific machine learning (SciML) can be leveraged to explore the interactions of physics at different spatial and temporal scales. This reprint presents scientific works on SciML-e.g., physics-guided neural networks, physics-informed neural networks, physics-encoded neural networks, and neural operators-for multi-scale multi-temporal structures and mechanisms with polymer behaviors (rheology, self-assembly, phase transition, etc.). This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condizione: new. Hardcover. Polymeric materials play a key role in supporting the ever-increasing demand for electronics, medicines, plastics, sensors, and the transition to renewable energy sources. This is achieved through polymers' distinct features at different structural and temporal scales (i.e., a subtle change in their atomic or mesoscopic structures leads to a totally emergent functionality). However, the design of new polymeric materials is still a lengthy process. This major challenge is related to their inability to comprehensively bridge phenomena that occur at temporal scales from tens of nanoseconds to seconds or spatial scales from nanometers to meters. Indeed, scientific datasets in this field are sparse and include only directly observable quantities, while the underlying processes are either too complex to observe directly or are completely unknown. To move towards an accelerated on-demand design for polymeric materials, recent breakthroughs in scientific machine learning (SciML) can be leveraged to explore the interactions of physics at different spatial and temporal scales. This reprint presents scientific works on SciML-e.g., physics-guided neural networks, physics-informed neural networks, physics-encoded neural networks, and neural operators-for multi-scale multi-temporal structures and mechanisms with polymer behaviors (rheology, self-assembly, phase transition, etc.). This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condizione: new. Hardcover. Polymeric materials play a key role in supporting the ever-increasing demand for electronics, medicines, plastics, sensors, and the transition to renewable energy sources. This is achieved through polymers' distinct features at different structural and temporal scales (i.e., a subtle change in their atomic or mesoscopic structures leads to a totally emergent functionality). However, the design of new polymeric materials is still a lengthy process. This major challenge is related to their inability to comprehensively bridge phenomena that occur at temporal scales from tens of nanoseconds to seconds or spatial scales from nanometers to meters. Indeed, scientific datasets in this field are sparse and include only directly observable quantities, while the underlying processes are either too complex to observe directly or are completely unknown. To move towards an accelerated on-demand design for polymeric materials, recent breakthroughs in scientific machine learning (SciML) can be leveraged to explore the interactions of physics at different spatial and temporal scales. This reprint presents scientific works on SciML-e.g., physics-guided neural networks, physics-informed neural networks, physics-encoded neural networks, and neural operators-for multi-scale multi-temporal structures and mechanisms with polymer behaviors (rheology, self-assembly, phase transition, etc.). This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Condizione: New. PRINT ON DEMAND.

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Polymeric materials play a key role in supporting the ever-increasing demand for electronics, medicines, plastics, sensors, and the transition to renewable energy sources. This is achieved through polymers' distinct features at different structural and temporal scales (i.e., a subtle change in their atomic or mesoscopic structures leads to a totally emergent functionality). However, the design of new polymeric materials is still a lengthy process. This major challenge is related to their inability to comprehensively bridge phenomena that occur at temporal scales from tens of nanoseconds to seconds or spatial scales from nanometers to meters. Indeed, scientific datasets in this field are sparse and include only directly observable quantities, while the underlying processes are either too complex to observe directly or are completely unknown. To move towards an accelerated on-demand design for polymeric materials, recent breakthroughs in scientific machine learning (SciML) can be leveraged to explore the interactions of physics at different spatial and temporal scales. This reprint presents scientific works on SciML-e.g., physics-guided neural networks, physics-informed neural networks, physics-encoded neural networks, and neural operators-for multi-scale multi-temporal structures and mechanisms with polymer behaviors (rheology, self-assembly, phase transition, etc.).

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Polymer-processing techniques are of the utmost importance for producing polymeric parts. They must produce parts with the desired qualities, which are usually related to mechanical performance, dimensional conformity, and appearance. Aiming to maximize the overall efficiency of the polymer-processing techniques, advanced modeling codes along with experimental measurements are needed to simulate and optimize the processes.Thus, this reprint exploits the digital transformation of the plastics industry, both through the creation of more robust and accurate modeling tools and the development of cutting-edge experimental techniques. Furthermore, it addresses advanced topics, such as crystallization during the solidification processes, prediction of fiber orientation in the cases of short and long fiber composites, prediction of the foaming process (such as microcellular foaming), and flow instabilities by including viscoelastic constitutive equations.

Buch. Condizione: Neu. Advanced Polymer Simulation and Processing | Volume I | Buch | Englisch | 2023 | MDPI AG | EAN 9783036566641 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condizione: Neu. Advanced Polymer Simulation and Processing | Volume II | Buch | Englisch | 2023 | MDPI AG | EAN 9783036566665 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.